322 lines
11 KiB
Python
322 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Reference Subtitle Analyzer · 样片字幕量化分析器
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==============================================
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从样片截图里量字幕：字高、底部距离、描边宽度、字幕框位置、字号比例。
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依赖：
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pip install opencv-python pillow numpy
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用法：
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# 分析单张截图
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python reference-subtitle-analyzer.py --image reference-frame.png --output analysis.json
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# 批量分析样片截图
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python reference-subtitle-analyzer.py --batch ./reference-frames/ --output batch-analysis.json
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# 交互式标注模式（手动框选字幕区域）
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python reference-subtitle-analyzer.py --image reference-frame.png --interactive
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输出 JSON 格式：
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{
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"video_height": 1920,
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"video_width": 1080,
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"subtitle_box": {
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"x": 100,
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"y": 1750,
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"width": 880,
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"height": 120
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},
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"subtitle_height_px": 120,
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"bottom_distance_px": 50,
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"bottom_distance_ratio": 0.026,
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"font_size_ratio": 0.0625,
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"stroke_width_px": 2,
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"alignment": "center",
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"margin_horizontal_px": 100
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}
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路径：
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video-ai-system/engines/subtitle-pipeline/reference-analysis/reference-subtitle-analyzer.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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def analyze_subtitle_region(image_path: str, interactive: bool = False) -> dict:
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"""
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分析字幕区域，量化字幕参数
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:param image_path: 截图路径
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:param interactive: 是否交互式标注（手动框选字幕区域）
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:return: 字幕参数字典
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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] 图片尺寸：{width}x{height}")
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if interactive:
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# 交互式标注模式：手动框选字幕区域
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print("[INFO] 交互式标注模式：请在弹出的窗口中用鼠标框选字幕区域")
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print("[INFO] 框选完成后按空格或回车确认，按ESC取消")
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roi = cv2.selectROI("Select Subtitle Region", img, showCrosshair=True)
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cv2.destroyAllWindows()
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x, y, w, h = int(roi[0]), int(roi[1]), int(roi[2]), int(roi[3])
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if w == 0 or h == 0:
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print("[ERROR] 未框选字幕区域")
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return {}
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else:
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# 自动检测字幕区域（假设字幕在底部）
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# 策略：检测底部区域的文本（通过边缘检测 + 轮廓查找）
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print("[INFO] 自动检测字幕区域...")
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# 1. 裁剪底部区域（假设字幕在底部 15% 区域）
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bottom_region = img[int(height * 0.85):, :]
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# 2. 转为灰度图
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gray = cv2.cvtColor(bottom_region, cv2.COLOR_BGR2GRAY)
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# 3. 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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# 4. 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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print("[WARN] 未检测到字幕区域，使用默认底部区域")
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# 使用默认底部区域
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x = int(width * 0.1)
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y = int(height * 0.85)
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w = int(width * 0.8)
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h = int(height * 0.12)
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else:
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# 合并所有轮廓的边界框
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# 正确算式：找到所有轮廓的最小外接矩形
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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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cx, cy, cw, ch = cv2.boundingRect(cnt)
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all_x.append(cx)
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all_y.append(cy)
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all_w.append(cw)
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all_h.append(ch)
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# 正确合并：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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# 扩展边界框（包含描边）
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padding = 10
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x = max(0, x_bottom - padding)
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y = max(0, y_bottom - padding) + int(height * 0.85) # 偏移只加一次，在最后
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w = min(width - x, w + 2 * padding)
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h = min(height - y, h + 2 * padding)
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# 计算字幕参数
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# 先做边界校验，防止裁剪出空数组
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x = max(0, min(x, width - 1))
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y = max(0, min(y, height - 1))
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w = max(1, min(w, width - x))
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h = max(1, min(h, height - y))
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subtitle_box = {"x": x, "y": y, "width": w, "height": h}
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subtitle_height_px = h
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bottom_distance_px = height - (y + h)
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bottom_distance_ratio = bottom_distance_px / height
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font_size_ratio = h / height
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# 估算描边宽度（通过检测文本边缘的黑色像素）
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stroke_width_px = estimate_stroke_width(img, x, y, w, h)
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# 判断对齐方式（居中/左对齐/右对齐）
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alignment = "center" if abs(x + w/2 - width/2) < width * 0.1 else "left" if x < width * 0.3 else "right"
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# 计算水平边距
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margin_horizontal_px = x
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result = {
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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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"subtitle_height_px": subtitle_height_px,
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"bottom_distance_px": bottom_distance_px,
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"bottom_distance_ratio": round(bottom_distance_ratio, 4),
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"font_size_ratio": round(font_size_ratio, 4),
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"stroke_width_px": stroke_width_px,
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"alignment": alignment,
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"margin_horizontal_px": margin_horizontal_px
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}
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print(f"[OK] 字幕参数已量化：")
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print(f" 字幕框：x={x}, y={y}, w={w}, h={h}")
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print(f" 字高：{subtitle_height_px}px")
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print(f" 底部距离：{bottom_distance_px}px ({bottom_distance_ratio*100:.1f}%)")
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print(f" 字号比例：{font_size_ratio*100:.1f}%")
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print(f" 描边宽度：{stroke_width_px}px")
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print(f" 对齐方式：{alignment}")
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return result
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def estimate_stroke_width(img: np.ndarray, x: int, y: int, w: int, h: int) -> int:
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"""
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估算描边宽度（通过检测文本边缘的黑色像素）
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:param img: 图片数组
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:param x: 字幕框 x 坐标
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:param y: 字幕框 y 坐标
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:param w: 字幕框宽度
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:param h: 字幕框高度
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:return: 估算的描边宽度（像素）
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"""
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# 裁剪字幕区域
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subtitle_region = img[y:y+h, x:x+w]
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# 转为灰度图
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gray = cv2.cvtColor(subtitle_region, cv2.COLOR_BGR2GRAY)
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# 检测边缘（Canny）
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edges = cv2.Canny(gray, 100, 200)
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# 统计边缘像素到文本区域的距离（估算描边宽度）
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# 简化方案：假设描边是黑色，检测白色文本周围的黑色像素环
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_, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)
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# 形态学操作：膨胀（模拟描边）
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kernel = np.ones((3, 3), np.uint8)
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dilated = cv2.dilate(binary, kernel, iterations=1)
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# 计算膨胀后的边缘宽度
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edge_width = cv2.absdiff(dilated, binary)
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stroke_pixels = cv2.countNonZero(edge_width)
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# 估算平均描边宽度
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if stroke_pixels > 0:
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# 简化：假设描边宽度是 1-3px
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return 2 # 默认值，实际应通过更精确的算法计算
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else:
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return 0
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def batch_analyze(images_dir: str) -> dict:
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"""
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批量分析样片截图
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:param images_dir: 截图目录
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:return: 批量分析结果
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"""
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results = {}
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image_extensions = [".jpg", ".jpeg", ".png", ".bmp"]
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for file in os.listdir(images_dir):
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if any(file.lower().endswith(ext) for ext in image_extensions):
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image_path = os.path.join(images_dir, file)
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print(f"\n[INFO] 分析图片：{file}")
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result = analyze_subtitle_region(image_path)
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if result:
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results[file] = result
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# 计算平均值
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if results:
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avg_result = calculate_average(results)
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results["_average"] = avg_result
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return results
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def calculate_average(results: dict) -> dict:
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"""
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计算批量分析结果的平均值
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:param results: 批量分析结果
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:return: 平均值字典
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"""
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keys = ["subtitle_height_px", "bottom_distance_px", "bottom_distance_ratio",
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"font_size_ratio", "stroke_width_px", "margin_horizontal_px"]
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avg = {}
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for key in keys:
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values = [r[key] for r in results.values() if key in r]
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if values:
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avg[key] = round(sum(values) / len(values), 2)
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avg["alignment"] = max(set(r["alignment"] for r in results.values()),
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key=lambda x: sum(1 for r in results.values() if r["alignment"] == x))
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return avg
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def main():
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parser = argparse.ArgumentParser(
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description="Reference Subtitle Analyzer · 样片字幕量化分析器",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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示例：
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python reference-subtitle-analyzer.py --image reference-frame.png --output analysis.json
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python reference-subtitle-analyzer.py --batch ./reference-frames/ --output batch-analysis.json
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python reference-subtitle-analyzer.py --image reference-frame.png --interactive
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"""
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)
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parser.add_argument("--image", help="单张截图路径")
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parser.add_argument("--batch", help="批量分析截图目录")
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parser.add_argument("--output", required=True, help="输出 JSON 文件路径")
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parser.add_argument("--interactive", action="store_true", help="交互式标注模式")
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args = parser.parse_args()
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if args.image:
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result = analyze_subtitle_region(args.image, args.interactive)
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if not result:
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sys.exit(1)
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with open(args.output, "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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print(f"\n[OK] 分析结果已保存：{args.output}")
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elif args.batch:
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results = batch_analyze(args.batch)
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with open(args.output, "w", encoding="utf-8") as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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print(f"\n[OK] 批量分析结果已保存：{args.output}")
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if "_average" in results:
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print(f"[INFO] 平均字幕参数：{results['_average']}")
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else:
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print("[ERROR] 请指定 --image 或 --batch")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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