588 lines
20 KiB
Python
588 lines
20 KiB
Python
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import cv2
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import numpy as np
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import os
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import subprocess
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import shutil
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from datetime import timedelta
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import argparse
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from sklearn.metrics.pairwise import cosine_similarity
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from skimage.metrics import structural_similarity as ssim
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from scipy import stats
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from collections import deque
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# 设置固定的输入输出路径
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INPUT_VIDEO_PATH = "/root/autodl-tmp/kuaishou_demo"
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OUTPUT_DIR = "/root/autodl-tmp/02_VideoSplitter/VideoSplitter_output"
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# 支持的视频格式
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VIDEO_EXTENSIONS = ['.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv']
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# 增强参数设置
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SAMPLE_RATE = 1
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METHOD = "enhanced" # 新增enhanced方法
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THRESHOLD = 0.5
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VERBOSE = True
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# 新增参数
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WINDOW_SIZE = 30 # 滑动窗口大小
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GRADIENT_THRESHOLD = 0.02 # 渐变检测阈值
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EDGE_DENSITY_THRESHOLD = 0.3 # 边缘密度变化阈值
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COLOR_HIST_THRESHOLD = 0.4 # 颜色直方图变化阈值
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# FFMPEG可能的路径
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FFMPEG_PATHS = [
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'ffmpeg',
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'/usr/bin/ffmpeg',
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'/usr/local/bin/ffmpeg',
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'C:\\ffmpeg\\bin\\ffmpeg.exe',
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]
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def find_ffmpeg():
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"""查找系统中可用的ffmpeg路径"""
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try:
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if os.name == 'nt':
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result = subprocess.run(['where', 'ffmpeg'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
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if result.returncode == 0:
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return result.stdout.strip().split('\n')[0]
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else:
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result = subprocess.run(['which', 'ffmpeg'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
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if result.returncode == 0:
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return result.stdout.strip()
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except Exception:
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pass
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for path in FFMPEG_PATHS:
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if shutil.which(path):
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return path
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return None
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def extract_enhanced_features(frame):
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"""
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提取增强特征用于场景检测
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Args:
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frame: 输入帧
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Returns:
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features: 特征字典
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"""
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# 调整大小以加快处理
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frame_resized = cv2.resize(frame, (320, 180))
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# 1. 灰度图
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gray = cv2.cvtColor(frame_resized, cv2.COLOR_BGR2GRAY)
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# 2. 颜色直方图（HSV）
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hsv = cv2.cvtColor(frame_resized, cv2.COLOR_BGR2HSV)
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hist_h = cv2.calcHist([hsv], [0], None, [50], [0, 180])
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hist_s = cv2.calcHist([hsv], [1], None, [50], [0, 256])
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hist_v = cv2.calcHist([hsv], [2], None, [50], [0, 256])
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# 3. 边缘检测
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edges = cv2.Canny(gray, 50, 150)
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edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1])
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# 4. 亮度均值和标准差
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brightness_mean = np.mean(gray)
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brightness_std = np.std(gray)
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# 5. 纹理特征（局部二值模式的简化版本）
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sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
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sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
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texture_energy = np.mean(np.sqrt(sobel_x**2 + sobel_y**2))
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return {
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'gray': gray,
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'hist_h': hist_h.flatten(),
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'hist_s': hist_s.flatten(),
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'hist_v': hist_v.flatten(),
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'edge_density': edge_density,
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'brightness_mean': brightness_mean,
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'brightness_std': brightness_std,
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'texture_energy': texture_energy
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}
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def enhanced_frame_similarity(features1, features2):
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"""
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增强的帧相似度计算
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Args:
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features1, features2: 特征字典
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Returns:
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similarity_scores: 各种相似度分数的字典
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"""
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scores = {}
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# 1. SSIM相似度
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scores['ssim'] = ssim(features1['gray'], features2['gray'])
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# 2. 颜色直方图相似度
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scores['hist_h'] = cv2.compareHist(features1['hist_h'], features2['hist_h'], cv2.HISTCMP_CORREL)
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scores['hist_s'] = cv2.compareHist(features1['hist_s'], features2['hist_s'], cv2.HISTCMP_CORREL)
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scores['hist_v'] = cv2.compareHist(features1['hist_v'], features2['hist_v'], cv2.HISTCMP_CORREL)
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# 3. 边缘密度变化
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edge_diff = abs(features1['edge_density'] - features2['edge_density'])
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scores['edge_stability'] = 1.0 - min(edge_diff / 0.5, 1.0) # 归一化
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# 4. 亮度稳定性
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brightness_diff = abs(features1['brightness_mean'] - features2['brightness_mean']) / 255.0
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scores['brightness_stability'] = 1.0 - brightness_diff
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# 5. 纹理稳定性
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texture_diff = abs(features1['texture_energy'] - features2['texture_energy'])
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scores['texture_stability'] = 1.0 - min(texture_diff / 100.0, 1.0) # 归一化
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return scores
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def detect_transition_type(similarity_window, frame_indices):
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"""
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检测转场类型
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Args:
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similarity_window: 相似度时间序列窗口
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frame_indices: 对应的帧索引
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Returns:
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transition_info: 转场信息字典
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"""
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if len(similarity_window) < 5:
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return {'type': 'unknown', 'confidence': 0.0}
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# 计算相似度变化趋势
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x = np.arange(len(similarity_window))
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slope, intercept, r_value, p_value, std_err = stats.linregress(x, similarity_window)
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# 计算变化率
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diff = np.diff(similarity_window)
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max_drop = np.min(diff) if len(diff) > 0 else 0
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total_change = similarity_window[-1] - similarity_window[0]
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transition_info = {
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'slope': slope,
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'r_squared': r_value**2,
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'max_drop': max_drop,
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'total_change': total_change,
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'std': np.std(similarity_window)
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}
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# 分类转场类型
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if r_value**2 > 0.7 and slope < -0.02:
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# 线性下降，可能是渐变
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if abs(max_drop) < 0.1:
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transition_info.update({'type': 'fade', 'confidence': 0.8})
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else:
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transition_info.update({'type': 'dissolve', 'confidence': 0.7})
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elif abs(max_drop) > 0.3:
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# 突然下降，硬切
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transition_info.update({'type': 'cut', 'confidence': 0.9})
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elif np.std(similarity_window) > 0.1 and total_change < -0.2:
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# 不规则变化，可能是复杂转场
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transition_info.update({'type': 'complex', 'confidence': 0.6})
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else:
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transition_info.update({'type': 'stable', 'confidence': 0.5})
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return transition_info
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def enhanced_scene_detection(frames_info, method='enhanced', threshold=0.5):
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"""
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增强的场景变化检测
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Args:
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frames_info: 帧信息列表
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method: 检测方法
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threshold: 基础阈值
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Returns:
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scenes: 场景信息列表，包含转场类型
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"""
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if len(frames_info) < WINDOW_SIZE:
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return []
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print("正在提取增强特征...")
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features_list = []
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# 提取所有帧的特征
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for i, (frame_num, timestamp, frame_path) in enumerate(frames_info):
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frame = cv2.imread(frame_path)
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features = extract_enhanced_features(frame)
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features_list.append(features)
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if i % 50 == 0:
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print(f"特征提取进度: {i+1}/{len(frames_info)}")
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print("正在进行增强场景检测...")
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# 滑动窗口分析
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scenes = []
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scene_start = frames_info[0]
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similarity_window = deque(maxlen=WINDOW_SIZE)
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composite_scores = []
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for i in range(1, len(frames_info)):
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# 计算多维相似度
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sim_scores = enhanced_frame_similarity(features_list[i-1], features_list[i])
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# 计算复合相似度分数
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composite_score = (
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sim_scores['ssim'] * 0.3 +
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(sim_scores['hist_h'] + sim_scores['hist_s'] + sim_scores['hist_v']) / 3 * 0.25 +
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sim_scores['edge_stability'] * 0.15 +
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sim_scores['brightness_stability'] * 0.15 +
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sim_scores['texture_stability'] * 0.15
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)
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composite_scores.append(composite_score)
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similarity_window.append(composite_score)
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# 自适应阈值
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if len(composite_scores) > 50:
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recent_scores = composite_scores[-50:]
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adaptive_threshold = np.mean(recent_scores) - 2 * np.std(recent_scores)
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adaptive_threshold = max(adaptive_threshold, threshold * 0.5) # 设置下限
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else:
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adaptive_threshold = threshold
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# 检测场景变化
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if composite_score < adaptive_threshold and len(similarity_window) >= 10:
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# 分析转场类型
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transition_info = detect_transition_type(
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list(similarity_window)[-10:],
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list(range(i-9, i+1))
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)
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scene_end = frames_info[i-1]
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scene_duration = scene_end[1] - scene_start[1]
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# 根据转场类型调整最小时长要求
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min_duration = 1.0 if transition_info['type'] == 'cut' else 2.0
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if scene_duration >= min_duration:
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scenes.append({
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'start_frame': scene_start[0],
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'end_frame': scene_end[0],
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'start_time': scene_start[1],
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'end_time': scene_end[1],
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'duration': scene_duration,
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'transition_type': transition_info['type'],
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'transition_confidence': transition_info['confidence'],
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'similarity_score': composite_score,
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'adaptive_threshold': adaptive_threshold
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})
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if VERBOSE:
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print(f"检测到{transition_info['type']}转场: 帧 {scene_end[0]}, "
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f"时间 {timedelta(seconds=scene_end[1])}, "
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f"相似度: {composite_score:.4f}, "
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f"置信度: {transition_info['confidence']:.2f}")
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scene_start = frames_info[i]
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similarity_window.clear() # 清空窗口重新开始
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# 添加最后一个场景
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if len(frames_info) > 0:
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scene_end = frames_info[-1]
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scene_duration = scene_end[1] - scene_start[1]
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if scene_duration >= 1.0:
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scenes.append({
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'start_frame': scene_start[0],
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'end_frame': scene_end[0],
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'start_time': scene_start[1],
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'end_time': scene_end[1],
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'duration': scene_duration,
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'transition_type': 'end',
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'transition_confidence': 1.0,
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'similarity_score': 1.0,
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'adaptive_threshold': threshold
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})
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# 统计转场类型
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transition_types = {}
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for scene in scenes:
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t_type = scene['transition_type']
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transition_types[t_type] = transition_types.get(t_type, 0) + 1
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print(f"\n增强场景检测统计:")
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print(f"检测到 {len(scenes)} 个场景, 平均时长: {sum(s['duration'] for s in scenes)/max(1, len(scenes)):.2f}秒")
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print("转场类型分析:")
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for t_type, count in transition_types.items():
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print(f" {t_type}: {count} 个")
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return scenes
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def extract_frames(video_path, output_dir, sample_rate=1):
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"""保持原有的帧提取功能"""
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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cap = cv2.VideoCapture(video_path)
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fps = cap.get(cv2.CAP_PROP_FPS)
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frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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duration = frame_count / fps
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print(f"视频信息：{frame_count}帧, {fps}fps, 时长:{timedelta(seconds=duration)}")
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|
|||
|
|
frames_info = []
|
|||
|
|
frame_number = 0
|
|||
|
|
saved_count = 0
|
|||
|
|
|
|||
|
|
while True:
|
|||
|
|
ret, frame = cap.read()
|
|||
|
|
if not ret:
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
if frame_number % sample_rate == 0:
|
|||
|
|
timestamp = frame_number / fps
|
|||
|
|
frame_path = os.path.join(output_dir, f"frame_{saved_count:05d}.jpg")
|
|||
|
|
cv2.imwrite(frame_path, frame)
|
|||
|
|
frames_info.append((frame_number, timestamp, frame_path))
|
|||
|
|
saved_count += 1
|
|||
|
|
|
|||
|
|
frame_number += 1
|
|||
|
|
|
|||
|
|
if frame_number % 100 == 0:
|
|||
|
|
print(f"处理进度: {frame_number}/{frame_count} ({frame_number/frame_count*100:.2f}%)")
|
|||
|
|
|
|||
|
|
cap.release()
|
|||
|
|
print(f"共提取了 {saved_count} 帧")
|
|||
|
|
|
|||
|
|
return frames_info
|
|||
|
|
|
|||
|
|
def get_video_files(directory):
|
|||
|
|
"""获取目录中所有视频文件"""
|
|||
|
|
video_files = []
|
|||
|
|
|
|||
|
|
if os.path.isfile(directory):
|
|||
|
|
ext = os.path.splitext(directory)[1].lower()
|
|||
|
|
if ext in VIDEO_EXTENSIONS:
|
|||
|
|
return [directory]
|
|||
|
|
|
|||
|
|
for root, _, files in os.walk(directory):
|
|||
|
|
for file in files:
|
|||
|
|
ext = os.path.splitext(file)[1].lower()
|
|||
|
|
if ext in VIDEO_EXTENSIONS:
|
|||
|
|
video_files.append(os.path.join(root, file))
|
|||
|
|
|
|||
|
|
return video_files
|
|||
|
|
|
|||
|
|
def get_parent_folder_name(path):
|
|||
|
|
"""获取路径中'video'上一级文件夹的名字"""
|
|||
|
|
abs_path = os.path.abspath(path)
|
|||
|
|
if os.path.isdir(abs_path):
|
|||
|
|
parent = os.path.dirname(abs_path.rstrip('/'))
|
|||
|
|
folder_name = os.path.basename(parent)
|
|||
|
|
else:
|
|||
|
|
parent = os.path.dirname(os.path.dirname(abs_path))
|
|||
|
|
folder_name = os.path.basename(parent)
|
|||
|
|
return folder_name
|
|||
|
|
|
|||
|
|
def process_single_video(video_path, output_base_dir):
|
|||
|
|
"""
|
|||
|
|
处理单个视频文件
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
video_path: 视频文件路径
|
|||
|
|
output_base_dir: 输出基础目录
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
dict: 处理结果信息
|
|||
|
|
"""
|
|||
|
|
print(f"\n{'='*60}")
|
|||
|
|
print(f"正在处理视频: {os.path.basename(video_path)}")
|
|||
|
|
print(f"{'='*60}")
|
|||
|
|
|
|||
|
|
# 创建输出目录
|
|||
|
|
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
|||
|
|
output_dir = os.path.join(output_base_dir, video_name)
|
|||
|
|
|
|||
|
|
if not os.path.exists(output_dir):
|
|||
|
|
os.makedirs(output_dir)
|
|||
|
|
|
|||
|
|
# 提取帧
|
|||
|
|
frames_dir = os.path.join(output_dir, "frames")
|
|||
|
|
print(f"正在提取帧到: {frames_dir}")
|
|||
|
|
frames_info = extract_frames(video_path, frames_dir, SAMPLE_RATE)
|
|||
|
|
|
|||
|
|
if not frames_info:
|
|||
|
|
print(f"警告: 无法从视频 {video_path} 提取帧")
|
|||
|
|
return {
|
|||
|
|
'video_path': video_path,
|
|||
|
|
'status': 'failed',
|
|||
|
|
'error': '无法提取帧',
|
|||
|
|
'scenes': []
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 场景检测
|
|||
|
|
print(f"正在进行场景检测...")
|
|||
|
|
scenes = enhanced_scene_detection(frames_info, METHOD, THRESHOLD)
|
|||
|
|
|
|||
|
|
# 生成详细报告
|
|||
|
|
report_path = os.path.join(output_dir, f"{video_name}_analysis_report.txt")
|
|||
|
|
generate_detailed_report(video_path, frames_info, scenes, report_path)
|
|||
|
|
|
|||
|
|
# 生成JSON格式的切割信息
|
|||
|
|
json_path = os.path.join(output_dir, f"{video_name}_scenes.json")
|
|||
|
|
generate_scenes_json(video_path, scenes, json_path)
|
|||
|
|
|
|||
|
|
print(f"处理完成: {video_name}")
|
|||
|
|
print(f"检测到 {len(scenes)} 个场景")
|
|||
|
|
print(f"报告文件: {report_path}")
|
|||
|
|
print(f"JSON文件: {json_path}")
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
'video_path': video_path,
|
|||
|
|
'status': 'success',
|
|||
|
|
'scenes_count': len(scenes),
|
|||
|
|
'scenes': scenes,
|
|||
|
|
'report_path': report_path,
|
|||
|
|
'json_path': json_path
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def generate_detailed_report(video_path, frames_info, scenes, report_path):
|
|||
|
|
"""生成详细的文本报告"""
|
|||
|
|
video_name = os.path.basename(video_path)
|
|||
|
|
|
|||
|
|
with open(report_path, 'w', encoding='utf-8') as f:
|
|||
|
|
f.write("增强视频转场分析报告\n")
|
|||
|
|
f.write("=" * 60 + "\n\n")
|
|||
|
|
|
|||
|
|
f.write(f"视频文件: {video_name}\n")
|
|||
|
|
f.write(f"视频路径: {video_path}\n")
|
|||
|
|
f.write(f"分析时间: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
|
|||
|
|
f.write(f"总帧数: {len(frames_info)}\n")
|
|||
|
|
f.write(f"检测到场景数: {len(scenes)}\n\n")
|
|||
|
|
|
|||
|
|
# 转场类型统计
|
|||
|
|
transition_stats = {}
|
|||
|
|
for scene in scenes:
|
|||
|
|
t_type = scene['transition_type']
|
|||
|
|
transition_stats[t_type] = transition_stats.get(t_type, 0) + 1
|
|||
|
|
|
|||
|
|
f.write("转场类型统计:\n")
|
|||
|
|
f.write("-" * 30 + "\n")
|
|||
|
|
for t_type, count in transition_stats.items():
|
|||
|
|
percentage = (count / len(scenes)) * 100 if scenes else 0
|
|||
|
|
f.write(f"{t_type}: {count} 个 ({percentage:.1f}%)\n")
|
|||
|
|
f.write("\n")
|
|||
|
|
|
|||
|
|
# 场景时长统计
|
|||
|
|
durations = [scene['duration'] for scene in scenes]
|
|||
|
|
if durations:
|
|||
|
|
f.write("场景时长统计:\n")
|
|||
|
|
f.write("-" * 30 + "\n")
|
|||
|
|
f.write(f"平均时长: {np.mean(durations):.2f}秒\n")
|
|||
|
|
f.write(f"最短时长: {np.min(durations):.2f}秒\n")
|
|||
|
|
f.write(f"最长时长: {np.max(durations):.2f}秒\n")
|
|||
|
|
f.write(f"时长标准差: {np.std(durations):.2f}秒\n\n")
|
|||
|
|
|
|||
|
|
# 详细场景信息
|
|||
|
|
f.write("详细场景信息:\n")
|
|||
|
|
f.write("-" * 60 + "\n")
|
|||
|
|
for i, scene in enumerate(scenes, 1):
|
|||
|
|
f.write(f"场景 {i}:\n")
|
|||
|
|
f.write(f" 开始帧: {scene['start_frame']}\n")
|
|||
|
|
f.write(f" 结束帧: {scene['end_frame']}\n")
|
|||
|
|
f.write(f" 开始时间: {timedelta(seconds=scene['start_time'])}\n")
|
|||
|
|
f.write(f" 结束时间: {timedelta(seconds=scene['end_time'])}\n")
|
|||
|
|
f.write(f" 时长: {scene['duration']:.2f}秒\n")
|
|||
|
|
f.write(f" 转场类型: {scene['transition_type']}\n")
|
|||
|
|
f.write(f" 置信度: {scene['transition_confidence']:.3f}\n")
|
|||
|
|
f.write(f" 相似度分数: {scene['similarity_score']:.4f}\n")
|
|||
|
|
f.write(f" 自适应阈值: {scene['adaptive_threshold']:.4f}\n")
|
|||
|
|
f.write("\n")
|
|||
|
|
|
|||
|
|
def generate_scenes_json(video_path, scenes, json_path):
|
|||
|
|
"""生成JSON格式的切割信息"""
|
|||
|
|
import json
|
|||
|
|
|
|||
|
|
video_name = os.path.basename(video_path)
|
|||
|
|
|
|||
|
|
scenes_data = {
|
|||
|
|
'content': []
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
for i, scene in enumerate(scenes, 1):
|
|||
|
|
scene_data = {
|
|||
|
|
'type': scene['transition_type'],
|
|||
|
|
'scene_index': i,
|
|||
|
|
'start_time': scene['start_time'],
|
|||
|
|
'end_time': scene['end_time'],
|
|||
|
|
'duration': scene['duration'],
|
|||
|
|
}
|
|||
|
|
scenes_data['scenes'].append(scene_data)
|
|||
|
|
|
|||
|
|
with open(json_path, 'w', encoding='utf-8') as f:
|
|||
|
|
json.dump(scenes_data, f, indent=2, ensure_ascii=False)
|
|||
|
|
|
|||
|
|
def batch_process_videos(input_dir, output_dir):
|
|||
|
|
"""
|
|||
|
|
批量处理视频文件，只生成每个视频的单独报告
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
input_dir: 输入目录路径
|
|||
|
|
output_dir: 输出目录路径
|
|||
|
|
"""
|
|||
|
|
print("=" * 80)
|
|||
|
|
print("增强视频转场分析工具 - 批量处理模式")
|
|||
|
|
print("=" * 80)
|
|||
|
|
|
|||
|
|
# 查找ffmpeg
|
|||
|
|
ffmpeg_path = find_ffmpeg()
|
|||
|
|
if ffmpeg_path:
|
|||
|
|
print(f"已找到ffmpeg: {ffmpeg_path}")
|
|||
|
|
else:
|
|||
|
|
print("警告: 未找到ffmpeg，某些功能可能受限")
|
|||
|
|
|
|||
|
|
# 获取所有视频文件
|
|||
|
|
video_files = get_video_files(input_dir)
|
|||
|
|
|
|||
|
|
if not video_files:
|
|||
|
|
print(f"错误: 在 '{input_dir}' 中没有找到视频文件")
|
|||
|
|
print(f"支持的格式: {', '.join(VIDEO_EXTENSIONS)}")
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
print(f"找到 {len(video_files)} 个视频文件:")
|
|||
|
|
for i, video_file in enumerate(video_files, 1):
|
|||
|
|
print(f" {i}. {os.path.basename(video_file)}")
|
|||
|
|
|
|||
|
|
# 创建输出目录
|
|||
|
|
if not os.path.exists(output_dir):
|
|||
|
|
os.makedirs(output_dir)
|
|||
|
|
|
|||
|
|
# 批量处理
|
|||
|
|
for i, video_path in enumerate(video_files, 1):
|
|||
|
|
print(f"\n处理进度: {i}/{len(video_files)}")
|
|||
|
|
try:
|
|||
|
|
process_single_video(video_path, output_dir)
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"处理视频 {os.path.basename(video_path)} 时出错: {e}")
|
|||
|
|
|
|||
|
|
print(f"\n{'='*80}")
|
|||
|
|
print("批量处理完成! 每个视频已生成单独报告。")
|
|||
|
|
print(f"{'='*80}")
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
"""主函数"""
|
|||
|
|
import datetime
|
|||
|
|
|
|||
|
|
# 直接使用默认参数进行批量处理
|
|||
|
|
print("增强视频转场分析工具 - 批量处理模式")
|
|||
|
|
print(f"输入目录: {INPUT_VIDEO_PATH}")
|
|||
|
|
print(f"输出目录: {OUTPUT_DIR}")
|
|||
|
|
print(f"采样率: {SAMPLE_RATE}")
|
|||
|
|
print(f"检测阈值: {THRESHOLD}")
|
|||
|
|
print(f"检测方法: {METHOD}")
|
|||
|
|
|
|||
|
|
# 检查输入路径
|
|||
|
|
if not os.path.exists(INPUT_VIDEO_PATH):
|
|||
|
|
print(f"错误: 输入路径不存在: {INPUT_VIDEO_PATH}")
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
# 执行批量处理
|
|||
|
|
batch_process_videos(INPUT_VIDEO_PATH, OUTPUT_DIR)
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
import datetime
|
|||
|
|
main()
|