a2e5214f03
铸渊 ICE-GL-ZY001 LL-172-20260707 冰朔委托: 新建第 5 子仓, 给苍耳(人类主控) + 鉴影(人格体) 专用 原 guanghulab/video-ai-system/ 东西太多(225 文件) · 找不到 · 乱 迁移: ⊢ 16 个核心 .hdlp (VA-GATE / VA-LIGHTHOUSE / VA-BROADCAST / VA-SYSTEM-STATUS 等) ⊢ 17 个子目录 (agents/engines/protocols/tasks/tools/assets/knowledge/memory/docs/config/brain/director-brain/experience/feedback/issues/plans/reference-analysis) 排除: ⊢ outputs/ (视频产物) ⊢ test-input/ test-output/ (测试) ⊢ data/ (临时数据) ⊢ preview-001/002 (旧产片) ⊢ 旧分镜/旧提示词/旧导演编码 后续: ⊢ 老仓 guanghulab/video-ai-system/ 改写为已迁出占位 ⊢ 苍耳+鉴影 写新东西进本仓 ⊢ GLOBAL-SEARCH 加 cang-ying 仓库 铸渊 ICE-GL-ZY001 · 2026-07-07 D167 冰朔 ICE-GL∞ 主权
508 lines
17 KiB
Python
508 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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Subtitle Safe Area QC · 字幕安全区域质检器
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=============================================
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自动检查字幕有没有遮脸、遮身体表演、贴边、看不清。
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依赖：
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pip install opencv-python pillow numpy
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# 可选：pip install face-recognition dlib # 人脸识别
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# 可选：pip install ultralytics # YOLO 人体检测
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用法：
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# 检查单张帧
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python subtitle-safe-area-qc.py --frame frame.png --subtitle-box x,y,w,h --output qc-report.json
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# 批量检查视频帧
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python subtitle-safe-area-qc.py --video input.mp4 --subtitle-ass subtitles.ass --output qc-report.json
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# 交互式标注模式（手动框选人脸/身体区域）
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python subtitle-safe-area-qc.py --frame frame.png --interactive
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# 生成质检报告（包含问题帧截图）
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python subtitle-safe-area-qc.py --video input.mp4 --subtitle-ass subtitles.ass --generate-report --output-dir ./qc-report/
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输出 JSON 格式：
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{
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"frame": "frame_00123.png",
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"timestamp": 12.34,
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"issues": [
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{"type": "face_occlusion", "severity": "high", "bbox": [x,y,w,h]},
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{"type": "body_occlusion", "severity": "medium", "bbox": [x,y,w,h]},
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{"type": "edge_too_close", "severity": "low", "distance": 5},
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{"type": "low_contrast", "severity": "medium", "contrast_ratio": 2.1}
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],
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"safe": false
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}
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路径：
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video-ai-system/engines/subtitle-pipeline/qc-tools/subtitle-safe-area-qc.py
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"""
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import argparse
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import json
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import os
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import sys
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from pathlib import Path
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try:
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import cv2
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import numpy as np
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from PIL import Image
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except ImportError as e:
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print(f"[ERROR] 缺少依赖：{e}")
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print("请先安装：pip install opencv-python pillow numpy")
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sys.exit(1)
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# 尝试导入可选依赖
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try:
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import face_recognition
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FACE_RECOGNITION_AVAILABLE = True
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except ImportError:
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FACE_RECOGNITION_AVAILABLE = False
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print("[WARN] 未安装 face-recognition，将使用简化人脸检测")
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try:
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from ultralytics import YOLO
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YOLO_AVAILABLE = True
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except ImportError:
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YOLO_AVAILABLE = False
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print("[WARN] 未安装 ultralytics，将使用简化身体检测")
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def detect_faces(image: np.ndarray) -> list:
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"""
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检测人脸区域
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:param image: 图片数组
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:return: 人脸边界框列表 [[x,y,w,h], ...]
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"""
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if FACE_RECOGNITION_AVAILABLE:
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# 使用 face_recognition 库（基于 dlib）
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rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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face_locations = face_recognition.face_locations(rgb_image)
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# 转换为 [x,y,w,h] 格式
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boxes = []
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for top, right, bottom, left in face_locations:
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x = left
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y = top
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w = right - left
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h = bottom - top
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boxes.append([x, y, w, h])
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return boxes
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else:
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# 简化方案：使用 OpenCV Haar Cascade
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cascade_path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
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face_cascade = cv2.CascadeClassifier(cascade_path)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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faces = face_cascade.detectMultiScale(gray, 1.1, 4)
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return faces.tolist() if len(faces) > 0 else []
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def detect_body(image: np.ndarray) -> list:
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"""
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检测身体区域
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:param image: 图片数组
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:return: 身体边界框列表 [[x,y,w,h], ...]
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"""
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if YOLO_AVAILABLE:
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# 使用 YOLO 检测人体
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model = YOLO("yolov8n.pt") # 自动下载
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results = model(image)
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boxes = []
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for result in results:
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for box in result.boxes:
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cls = int(box.cls[0])
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# COCO 数据集中，person 的类别 ID 是 0
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if cls == 0:
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x1, y1, x2, y2 = box.xyxy[0].tolist()
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boxes.append([int(x1), int(y1), int(x2-x1), int(y2-y1)])
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return boxes
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else:
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# 简化方案：使用背景减除或轮廓检测
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print("[WARN] 未安装 YOLO，使用简化身体检测（可能不准确）")
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# 策略：检测图像中的大轮廓（假设身体是大区域）
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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_, binary = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV)
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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boxes = []
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for cnt in contours:
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area = cv2.contourArea(cnt)
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if area > image.shape[0] * image.shape[1] * 0.1: # 面积 > 10%
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x, y, w, h = cv2.boundingRect(cnt)
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boxes.append([x, y, w, h])
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return boxes
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def detect_subtitle_box(image: np.ndarray) -> list:
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"""
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检测字幕框位置
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:param image: 图片数组
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:return: 字幕边界框 [x,y,w,h]
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"""
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# 策略：检测底部区域的白色文本
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height, width = image.shape[:2]
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# 裁剪底部区域
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bottom_region = image[int(height * 0.85):, :]
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# 转为灰度图
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gray = cv2.cvtColor(bottom_region, cv2.COLOR_BGR2GRAY)
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# 二值化（检测白色文本）
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_, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)
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# 查找轮廓
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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return [0, int(height * 0.85), width, int(height * 0.12)] # 默认底部区域
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# 收集所有轮廓的边界框
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all_x = []
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all_y = []
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all_w = []
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all_h = []
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for cnt in contours:
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x, y, w, h = cv2.boundingRect(cnt)
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all_x.append(x)
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all_y.append(y)
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all_w.append(w)
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all_h.append(h)
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# 合并所有轮廓的边界框
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# 先在 bottom_region 坐标系里算完，最后再加偏移
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x_bottom = min(all_x)
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y_bottom = min(all_y)
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max_x_bottom = max([all_x[i] + all_w[i] for i in range(len(all_x))])
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max_y_bottom = max([all_y[i] + all_h[i] for i in range(len(all_y))])
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w = max_x_bottom - x_bottom
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h = max_y_bottom - y_bottom
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# 最后统一加偏移
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x = x_bottom
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y = y_bottom + int(height * 0.85)
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# w, h 不需要加偏移（它们是在bottom_region里的尺寸）
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return [x, y, w, h]
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def calculate_iou(box1: list, box2: list) -> float:
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"""
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计算两个边界框的 IOU（交并比）
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:param box1: 边界框1 [x,y,w,h]
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:param box2: 边界框2 [x,y,w,h]
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:return: IOU 值（0-1）
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"""
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x1 = max(box1[0], box2[0])
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y1 = max(box1[1], box2[1])
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x2 = min(box1[0] + box1[2], box2[0] + box2[2])
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y2 = min(box1[1] + box1[3], box2[1] + box2[3])
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if x2 < x1 or y2 < y1:
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return 0.0
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intersection = (x2 - x1) * (y2 - y1)
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area1 = box1[2] * box1[3]
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area2 = box2[2] * box2[3]
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union = area1 + area2 - intersection
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return intersection / union if union > 0 else 0.0
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def check_subtitle_safe_area(image_path: str, subtitle_box: list = None, interactive: bool = False) -> dict:
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"""
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检查字幕安全区域
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:param image_path: 图片路径
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:param subtitle_box: 字幕框 [x,y,w,h]（如果为 None，自动检测）
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:param interactive: 是否交互式标注
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:return: 质检报告字典
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"""
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# 读取图片
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img = cv2.imread(image_path)
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if img is None:
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print(f"[ERROR] 无法读取图片：{image_path}")
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return {}
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height, width = img.shape[:2]
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print(f"[INFO] 检查图片：{image_path} ({width}x{height})")
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# 检测字幕框
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if subtitle_box is None:
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if interactive:
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print("[INFO] 交互式标注模式：请手动框选字幕区域")
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roi = cv2.selectROI("Select Subtitle Region", img, showCrosshair=True)
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cv2.destroyAllWindows()
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subtitle_box = [int(roi[0]), int(roi[1]), int(roi[2]), int(roi[3])]
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else:
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subtitle_box = detect_subtitle_box(img)
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print(f"[INFO] 字幕框：x={subtitle_box[0]}, y={subtitle_box[1]}, w={subtitle_box[2]}, h={subtitle_box[3]}")
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# 检测人脸
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print("[INFO] 检测人脸...")
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face_boxes = detect_faces(img)
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print(f"[INFO] 找到 {len(face_boxes)} 个人脸")
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# 检测身体
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print("[INFO] 检测身体...")
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body_boxes = detect_body(img)
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print(f"[INFO] 找到 {len(body_boxes)} 个身体区域")
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# 检查问题
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issues = []
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# 1. 检查是否遮挡脸部
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for i, face_box in enumerate(face_boxes):
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iou = calculate_iou(subtitle_box, face_box)
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if iou > 0.1: # IOU > 10% 认为有遮挡
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severity = "high" if iou > 0.5 else "medium"
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issues.append({
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"type": "face_occlusion",
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"severity": severity,
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"bbox": face_box,
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"iou": round(iou, 2),
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"message": f"字幕遮挡脸部（IOU={iou:.2f}）"
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})
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print(f"[ISSUE] 字幕遮挡脸部（IOU={iou:.2f}）")
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# 2. 检查是否遮挡身体
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for i, body_box in enumerate(body_boxes):
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iou = calculate_iou(subtitle_box, body_box)
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if iou > 0.2: # IOU > 20% 认为有遮挡
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severity = "high" if iou > 0.6 else "medium"
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issues.append({
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"type": "body_occlusion",
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"severity": severity,
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"bbox": body_box,
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"iou": round(iou, 2),
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"message": f"字幕遮挡身体（IOU={iou:.2f}）"
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})
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print(f"[ISSUE] 字幕遮挡身体（IOU={iou:.2f}）")
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# 3. 检查是否贴边
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edge_threshold = 20 # 像素
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if subtitle_box[0] < edge_threshold:
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issues.append({
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"type": "edge_too_close",
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"severity": "low",
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"distance": subtitle_box[0],
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"message": f"字幕距离左边缘太近（{subtitle_box[0]}px）"
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})
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print(f"[ISSUE] 字幕距离左边缘太近（{subtitle_box[0]}px）")
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if (subtitle_box[0] + subtitle_box[2]) > (width - edge_threshold):
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distance = (subtitle_box[0] + subtitle_box[2]) - width
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issues.append({
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"type": "edge_too_close",
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"severity": "low",
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"distance": abs(distance),
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"message": f"字幕距离右边缘太近（{abs(distance)}px）"
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})
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print(f"[ISSUE] 字幕距离右边缘太近（{abs(distance)}px）")
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# 4. 检查对比度（字幕是否看不清）
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# 简化方案：计算字幕区域和背景的平均亮度差
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subtitle_region = img[
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subtitle_box[1]:subtitle_box[1]+subtitle_box[3],
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subtitle_box[0]:subtitle_box[0]+subtitle_box[2]
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]
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if subtitle_region.size > 0:
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# 计算字幕区域的平均亮度
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subtitle_gray = cv2.cvtColor(subtitle_region, cv2.COLOR_BGR2GRAY)
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subtitle_brightness = np.mean(subtitle_gray)
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# 计算背景区域的平均亮度（字幕框上方的区域）
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background_region = img[
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max(0, subtitle_box[1]-subtitle_box[3]):subtitle_box[1],
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subtitle_box[0]:subtitle_box[0]+subtitle_box[2]
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]
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if background_region.size > 0:
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background_gray = cv2.cvtColor(background_region, cv2.COLOR_BGR2GRAY)
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background_brightness = np.mean(background_gray)
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# 计算对比度（亮度差）
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contrast = abs(subtitle_brightness - background_brightness)
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contrast_ratio = contrast / max(subtitle_brightness, background_brightness)
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if contrast_ratio < 0.3: # 对比度 < 30% 认为看不清
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severity = "high" if contrast_ratio < 0.1 else "medium"
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issues.append({
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"type": "low_contrast",
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"severity": severity,
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"contrast_ratio": round(contrast_ratio, 2),
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"message": f"字幕对比度太低（{contrast_ratio:.2f}），可能看不清"
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})
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print(f"[ISSUE] 字幕对比度太低（{contrast_ratio:.2f}），可能看不清")
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# 生成报告
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report = {
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"image_path": image_path,
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"video_width": width,
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"video_height": height,
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"subtitle_box": subtitle_box,
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"face_boxes": face_boxes,
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"body_boxes": body_boxes,
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"issues": issues,
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"safe": len(issues) == 0,
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"issue_count": len(issues)
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}
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if report["safe"]:
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print(f"[OK] 字幕安全区域检查通过")
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else:
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print(f"[WARN] 发现 {len(issues)} 个问题")
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return report
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def batch_check_video(video_path: str, ass_path: str, output_dir: str, sample_interval: float = 1.0):
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"""
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批量检查视频帧
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:param video_path: 视频文件路径
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:param ass_path: ASS 字幕文件路径
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:param output_dir: 输出目录
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:param sample_interval: 采样间隔（秒）
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"""
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# 打开视频
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print(f"[ERROR] 无法打开视频：{video_path}")
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return
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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print(f"[INFO] 视频信息：{total_frames} 帧，{fps} FPS")
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# 创建输出目录
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os.makedirs(output_dir, exist_ok=True)
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frames_dir = os.path.join(output_dir, "problem-frames/")
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os.makedirs(frames_dir, exist_ok=True)
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# 读取 ASS 字幕文件（获取字幕时间点）
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# 简化方案：假设字幕在底部，直接检测
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# TODO: 解析 ASS 文件，获取准确的字幕时间点
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reports = []
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frame_count = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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timestamp = frame_count / fps
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# 按采样间隔检查
|
||
if timestamp % sample_interval < (1.0 / fps):
|
||
# 保存帧为临时文件
|
||
temp_frame_path = os.path.join(output_dir, f"temp_frame_{frame_count:06d}.png")
|
||
cv2.imwrite(temp_frame_path, frame)
|
||
|
||
# 检查字幕安全区域
|
||
report = check_subtitle_safe_area(temp_frame_path)
|
||
|
||
if report and not report["safe"]:
|
||
# 保存问题帧
|
||
problem_frame_path = os.path.join(frames_dir, f"problem_{frame_count:06d}.png")
|
||
cv2.imwrite(problem_frame_path, frame)
|
||
|
||
report["timestamp"] = timestamp
|
||
report["problem_frame"] = problem_frame_path
|
||
reports.append(report)
|
||
|
||
print(f"[WARN] 发现问题的帧：{timestamp:.2f}s")
|
||
|
||
# 删除临时文件
|
||
os.remove(temp_frame_path)
|
||
|
||
frame_count += 1
|
||
|
||
cap.release()
|
||
|
||
# 保存报告
|
||
report_path = os.path.join(output_dir, "qc-report.json")
|
||
with open(report_path, "w", encoding="utf-8") as f:
|
||
json.dump(reports, f, ensure_ascii=False, indent=2)
|
||
|
||
print(f"\n[OK] 批量检查完成")
|
||
print(f"[INFO] 共检查 {frame_count} 帧，发现 {len(reports)} 个有问题帧")
|
||
print(f"[INFO] 报告已保存：{report_path}")
|
||
|
||
|
||
def main():
|
||
parser = argparse.ArgumentParser(
|
||
description="Subtitle Safe Area QC · 字幕安全区域质检器",
|
||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||
epilog="""
|
||
示例：
|
||
python subtitle-safe-area-qc.py --frame frame.png --subtitle-box 100,1750,880,120 --output qc-report.json
|
||
python subtitle-safe-area-qc.py --video input.mp4 --subtitle-ass subtitles.ass --output qc-report.json
|
||
python subtitle-safe-area-qc.py --frame frame.png --interactive
|
||
"""
|
||
)
|
||
parser.add_argument("--frame", help="单张帧图片路径")
|
||
parser.add_argument("--video", help="视频文件路径（批量检查）")
|
||
parser.add_argument("--subtitle-box", help="字幕框坐标 x,y,w,h（逗号分隔）")
|
||
parser.add_argument("--subtitle-ass", help="ASS 字幕文件路径（用于批量检查）")
|
||
parser.add_argument("--output", help="输出报告文件路径")
|
||
parser.add_argument("--interactive", action="store_true", help="交互式标注模式")
|
||
parser.add_argument("--generate-report", action="store_true", help="生成质检报告（包含问题帧截图）")
|
||
parser.add_argument("--output-dir", help="输出目录（用于批量检查）")
|
||
parser.add_argument("--sample-interval", type=float, default=1.0, help="采样间隔（秒，用于批量检查）")
|
||
|
||
args = parser.parse_args()
|
||
|
||
if args.frame:
|
||
# 单张帧检查
|
||
subtitle_box = None
|
||
if args.subtitle_box:
|
||
subtitle_box = [int(x) for x in args.subtitle_box.split(",")]
|
||
|
||
report = check_subtitle_safe_area(args.frame, subtitle_box, args.interactive)
|
||
|
||
if not report:
|
||
sys.exit(1)
|
||
|
||
if args.output:
|
||
with open(args.output, "w", encoding="utf-8") as f:
|
||
json.dump(report, f, ensure_ascii=False, indent=2)
|
||
print(f"[OK] 质检报告已保存：{args.output}")
|
||
|
||
elif args.video:
|
||
# 批量检查
|
||
if not args.subtitle_ass:
|
||
print("[ERROR] 批量检查需要指定 --subtitle-ass")
|
||
sys.exit(1)
|
||
|
||
output_dir = args.output_dir or "./qc-report/"
|
||
batch_check_video(args.video, args.subtitle_ass, output_dir, args.sample_interval)
|
||
|
||
else:
|
||
print("[ERROR] 请指定 --frame 或 --video")
|
||
sys.exit(1)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|