586 lines
20 KiB
Python
586 lines
20 KiB
Python
import cv2
|
||
import numpy as np
|
||
import os
|
||
import subprocess
|
||
import shutil
|
||
from datetime import timedelta
|
||
import argparse
|
||
from sklearn.metrics.pairwise import cosine_similarity
|
||
from skimage.metrics import structural_similarity as ssim
|
||
from scipy import stats
|
||
from collections import deque
|
||
import matplotlib.pyplot as plt
|
||
|
||
# 设置固定的输入输出路径
|
||
INPUT_VIDEO_PATH = "/root/autodl-tmp/kuaishou_demo"
|
||
OUTPUT_DIR = "/root/autodl-tmp/02_VideoSplitter/VideoSplitter_output"
|
||
|
||
# 支持的视频格式
|
||
VIDEO_EXTENSIONS = ['.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv']
|
||
|
||
# 增强参数设置
|
||
SAMPLE_RATE = 1
|
||
METHOD = "enhanced" # 新增enhanced方法
|
||
THRESHOLD = 0.5
|
||
VERBOSE = True
|
||
|
||
# 新增参数
|
||
WINDOW_SIZE = 30 # 滑动窗口大小
|
||
GRADIENT_THRESHOLD = 0.02 # 渐变检测阈值
|
||
EDGE_DENSITY_THRESHOLD = 0.3 # 边缘密度变化阈值
|
||
COLOR_HIST_THRESHOLD = 0.4 # 颜色直方图变化阈值
|
||
|
||
# FFMPEG可能的路径
|
||
FFMPEG_PATHS = [
|
||
'ffmpeg',
|
||
'/usr/bin/ffmpeg',
|
||
'/usr/local/bin/ffmpeg',
|
||
'C:\\ffmpeg\\bin\\ffmpeg.exe',
|
||
]
|
||
|
||
def find_ffmpeg():
|
||
"""查找系统中可用的ffmpeg路径"""
|
||
try:
|
||
if os.name == 'nt':
|
||
result = subprocess.run(['where', 'ffmpeg'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
|
||
if result.returncode == 0:
|
||
return result.stdout.strip().split('\n')[0]
|
||
else:
|
||
result = subprocess.run(['which', 'ffmpeg'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
|
||
if result.returncode == 0:
|
||
return result.stdout.strip()
|
||
except Exception:
|
||
pass
|
||
|
||
for path in FFMPEG_PATHS:
|
||
if shutil.which(path):
|
||
return path
|
||
|
||
return None
|
||
|
||
def extract_enhanced_features(frame):
|
||
"""
|
||
提取增强特征用于场景检测
|
||
|
||
Args:
|
||
frame: 输入帧
|
||
|
||
Returns:
|
||
features: 特征字典
|
||
"""
|
||
# 调整大小以加快处理
|
||
frame_resized = cv2.resize(frame, (320, 180))
|
||
|
||
# 1. 灰度图
|
||
gray = cv2.cvtColor(frame_resized, cv2.COLOR_BGR2GRAY)
|
||
|
||
# 2. 颜色直方图（HSV）
|
||
hsv = cv2.cvtColor(frame_resized, cv2.COLOR_BGR2HSV)
|
||
hist_h = cv2.calcHist([hsv], [0], None, [50], [0, 180])
|
||
hist_s = cv2.calcHist([hsv], [1], None, [50], [0, 256])
|
||
hist_v = cv2.calcHist([hsv], [2], None, [50], [0, 256])
|
||
|
||
# 3. 边缘检测
|
||
edges = cv2.Canny(gray, 50, 150)
|
||
edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1])
|
||
|
||
# 4. 亮度均值和标准差
|
||
brightness_mean = np.mean(gray)
|
||
brightness_std = np.std(gray)
|
||
|
||
# 5. 纹理特征（局部二值模式的简化版本）
|
||
sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
|
||
sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
|
||
texture_energy = np.mean(np.sqrt(sobel_x**2 + sobel_y**2))
|
||
|
||
return {
|
||
'gray': gray,
|
||
'hist_h': hist_h.flatten(),
|
||
'hist_s': hist_s.flatten(),
|
||
'hist_v': hist_v.flatten(),
|
||
'edge_density': edge_density,
|
||
'brightness_mean': brightness_mean,
|
||
'brightness_std': brightness_std,
|
||
'texture_energy': texture_energy
|
||
}
|
||
|
||
def enhanced_frame_similarity(features1, features2):
|
||
"""
|
||
增强的帧相似度计算
|
||
|
||
Args:
|
||
features1, features2: 特征字典
|
||
|
||
Returns:
|
||
similarity_scores: 各种相似度分数的字典
|
||
"""
|
||
scores = {}
|
||
|
||
# 1. SSIM相似度
|
||
scores['ssim'] = ssim(features1['gray'], features2['gray'])
|
||
|
||
# 2. 颜色直方图相似度
|
||
scores['hist_h'] = cv2.compareHist(features1['hist_h'], features2['hist_h'], cv2.HISTCMP_CORREL)
|
||
scores['hist_s'] = cv2.compareHist(features1['hist_s'], features2['hist_s'], cv2.HISTCMP_CORREL)
|
||
scores['hist_v'] = cv2.compareHist(features1['hist_v'], features2['hist_v'], cv2.HISTCMP_CORREL)
|
||
|
||
# 3. 边缘密度变化
|
||
edge_diff = abs(features1['edge_density'] - features2['edge_density'])
|
||
scores['edge_stability'] = 1.0 - min(edge_diff / 0.5, 1.0) # 归一化
|
||
|
||
# 4. 亮度稳定性
|
||
brightness_diff = abs(features1['brightness_mean'] - features2['brightness_mean']) / 255.0
|
||
scores['brightness_stability'] = 1.0 - brightness_diff
|
||
|
||
# 5. 纹理稳定性
|
||
texture_diff = abs(features1['texture_energy'] - features2['texture_energy'])
|
||
scores['texture_stability'] = 1.0 - min(texture_diff / 100.0, 1.0) # 归一化
|
||
|
||
return scores
|
||
|
||
def detect_transition_type(similarity_window, frame_indices):
|
||
"""
|
||
检测转场类型
|
||
|
||
Args:
|
||
similarity_window: 相似度时间序列窗口
|
||
frame_indices: 对应的帧索引
|
||
|
||
Returns:
|
||
transition_info: 转场信息字典
|
||
"""
|
||
if len(similarity_window) < 5:
|
||
return {'type': 'unknown', 'confidence': 0.0}
|
||
|
||
# 计算相似度变化趋势
|
||
x = np.arange(len(similarity_window))
|
||
slope, intercept, r_value, p_value, std_err = stats.linregress(x, similarity_window)
|
||
|
||
# 计算变化率
|
||
diff = np.diff(similarity_window)
|
||
max_drop = np.min(diff) if len(diff) > 0 else 0
|
||
total_change = similarity_window[-1] - similarity_window[0]
|
||
|
||
transition_info = {
|
||
'slope': slope,
|
||
'r_squared': r_value**2,
|
||
'max_drop': max_drop,
|
||
'total_change': total_change,
|
||
'std': np.std(similarity_window)
|
||
}
|
||
|
||
# 分类转场类型
|
||
if r_value**2 > 0.7 and slope < -0.02:
|
||
# 线性下降，可能是渐变
|
||
if abs(max_drop) < 0.1:
|
||
transition_info.update({'type': 'fade', 'confidence': 0.8})
|
||
else:
|
||
transition_info.update({'type': 'dissolve', 'confidence': 0.7})
|
||
elif abs(max_drop) > 0.3:
|
||
# 突然下降，硬切
|
||
transition_info.update({'type': 'cut', 'confidence': 0.9})
|
||
elif np.std(similarity_window) > 0.1 and total_change < -0.2:
|
||
# 不规则变化，可能是复杂转场
|
||
transition_info.update({'type': 'complex', 'confidence': 0.6})
|
||
else:
|
||
transition_info.update({'type': 'stable', 'confidence': 0.5})
|
||
|
||
return transition_info
|
||
|
||
def enhanced_scene_detection(frames_info, method='enhanced', threshold=0.5):
|
||
"""
|
||
增强的场景变化检测
|
||
|
||
Args:
|
||
frames_info: 帧信息列表
|
||
method: 检测方法
|
||
threshold: 基础阈值
|
||
|
||
Returns:
|
||
scenes: 场景信息列表，包含转场类型
|
||
"""
|
||
if len(frames_info) < WINDOW_SIZE:
|
||
return []
|
||
|
||
print("正在提取增强特征...")
|
||
features_list = []
|
||
|
||
# 提取所有帧的特征
|
||
for i, (frame_num, timestamp, frame_path) in enumerate(frames_info):
|
||
frame = cv2.imread(frame_path)
|
||
features = extract_enhanced_features(frame)
|
||
features_list.append(features)
|
||
|
||
if i % 50 == 0:
|
||
print(f"特征提取进度: {i+1}/{len(frames_info)}")
|
||
|
||
print("正在进行增强场景检测...")
|
||
|
||
# 滑动窗口分析
|
||
scenes = []
|
||
scene_start = frames_info[0]
|
||
similarity_window = deque(maxlen=WINDOW_SIZE)
|
||
composite_scores = []
|
||
|
||
for i in range(1, len(frames_info)):
|
||
# 计算多维相似度
|
||
sim_scores = enhanced_frame_similarity(features_list[i-1], features_list[i])
|
||
|
||
# 计算复合相似度分数
|
||
composite_score = (
|
||
sim_scores['ssim'] * 0.3 +
|
||
(sim_scores['hist_h'] + sim_scores['hist_s'] + sim_scores['hist_v']) / 3 * 0.25 +
|
||
sim_scores['edge_stability'] * 0.15 +
|
||
sim_scores['brightness_stability'] * 0.15 +
|
||
sim_scores['texture_stability'] * 0.15
|
||
)
|
||
|
||
composite_scores.append(composite_score)
|
||
similarity_window.append(composite_score)
|
||
|
||
# 自适应阈值
|
||
if len(composite_scores) > 50:
|
||
recent_scores = composite_scores[-50:]
|
||
adaptive_threshold = np.mean(recent_scores) - 2 * np.std(recent_scores)
|
||
adaptive_threshold = max(adaptive_threshold, threshold * 0.5) # 设置下限
|
||
else:
|
||
adaptive_threshold = threshold
|
||
|
||
# 检测场景变化
|
||
if composite_score < adaptive_threshold and len(similarity_window) >= 10:
|
||
# 分析转场类型
|
||
transition_info = detect_transition_type(
|
||
list(similarity_window)[-10:],
|
||
list(range(i-9, i+1))
|
||
)
|
||
|
||
scene_end = frames_info[i-1]
|
||
scene_duration = scene_end[1] - scene_start[1]
|
||
|
||
# 根据转场类型调整最小时长要求
|
||
min_duration = 1.0 if transition_info['type'] == 'cut' else 2.0
|
||
|
||
if scene_duration >= min_duration:
|
||
scenes.append({
|
||
'start_frame': scene_start[0],
|
||
'end_frame': scene_end[0],
|
||
'start_time': scene_start[1],
|
||
'end_time': scene_end[1],
|
||
'duration': scene_duration,
|
||
'transition_type': transition_info['type'],
|
||
'transition_confidence': transition_info['confidence'],
|
||
'similarity_score': composite_score,
|
||
'adaptive_threshold': adaptive_threshold
|
||
})
|
||
|
||
if VERBOSE:
|
||
print(f"检测到{transition_info['type']}转场: 帧 {scene_end[0]}, "
|
||
f"时间 {timedelta(seconds=scene_end[1])}, "
|
||
f"相似度: {composite_score:.4f}, "
|
||
f"置信度: {transition_info['confidence']:.2f}")
|
||
|
||
scene_start = frames_info[i]
|
||
similarity_window.clear() # 清空窗口重新开始
|
||
|
||
# 添加最后一个场景
|
||
if len(frames_info) > 0:
|
||
scene_end = frames_info[-1]
|
||
scene_duration = scene_end[1] - scene_start[1]
|
||
if scene_duration >= 1.0:
|
||
scenes.append({
|
||
'start_frame': scene_start[0],
|
||
'end_frame': scene_end[0],
|
||
'start_time': scene_start[1],
|
||
'end_time': scene_end[1],
|
||
'duration': scene_duration,
|
||
'transition_type': 'end',
|
||
'transition_confidence': 1.0,
|
||
'similarity_score': 1.0,
|
||
'adaptive_threshold': threshold
|
||
})
|
||
|
||
# 统计转场类型
|
||
transition_types = {}
|
||
for scene in scenes:
|
||
t_type = scene['transition_type']
|
||
transition_types[t_type] = transition_types.get(t_type, 0) + 1
|
||
|
||
print(f"\n增强场景检测统计:")
|
||
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")
|
||
print("转场类型分析:")
|
||
for t_type, count in transition_types.items():
|
||
print(f" {t_type}: {count} 个")
|
||
|
||
return scenes
|
||
|
||
def extract_frames(video_path, output_dir, sample_rate=1):
|
||
"""保持原有的帧提取功能"""
|
||
if not os.path.exists(output_dir):
|
||
os.makedirs(output_dir)
|
||
|
||
cap = cv2.VideoCapture(video_path)
|
||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||
duration = frame_count / fps
|
||
|
||
print(f"视频信息：{frame_count}帧, {fps}fps, 时长:{timedelta(seconds=duration)}")
|
||
|
||
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")
|
||
|
||
# 转场类型统计
|
||
transition_stats = {}
|
||
duration_by_type = {}
|
||
|
||
for clip in frames_info:
|
||
t_type = clip['transition_type']
|
||
transition_stats[t_type] = transition_stats.get(t_type, 0) + 1
|
||
if t_type not in duration_by_type:
|
||
duration_by_type[t_type] = []
|
||
duration_by_type[t_type].append(clip['duration'])
|
||
|
||
# 生成报告文件
|
||
report_file = os.path.join(output_dir, 'enhanced_analysis_report.txt')
|
||
with open(report_file, 'w', encoding='utf-8') as f:
|
||
f.write("增强视频切割分析报告\n")
|
||
f.write("=" * 50 + "\n\n")
|
||
|
||
f.write(f"\n详细片段信息:\n")
|
||
for clip in frames_info:
|
||
f.write(f"\"textIdx\":{clip['index']+1},\n")
|
||
f.write(f"\"time_start\":{clip["start"]},\n")
|
||
f.write(f"\"time_end\":{clip["end"]},\n")
|
||
f.write(f" 时长: {clip['duration']:.2f}秒\n")
|
||
|
||
print(f"已生成增强分析报告: {report_file}")
|
||
return frames_info
|
||
|
||
def extract_video_clips_enhanced(video_path, scenes, output_dir, ffmpeg_path=None):
|
||
"""
|
||
增强的视频片段提取，包含转场信息
|
||
"""
|
||
if not os.path.exists(output_dir):
|
||
os.makedirs(output_dir)
|
||
|
||
if ffmpeg_path is None:
|
||
ffmpeg_path = find_ffmpeg()
|
||
if ffmpeg_path is None:
|
||
print("错误: 找不到ffmpeg。")
|
||
return []
|
||
|
||
print(f"\n开始切割视频: {video_path}")
|
||
print(f"输出目录: {output_dir}")
|
||
print("-" * 60)
|
||
|
||
clips_info = []
|
||
|
||
for i, scene in enumerate(scenes):
|
||
start_time = scene['start_time']
|
||
end_time = scene['end_time']
|
||
duration = scene['duration']
|
||
transition_type = scene['transition_type']
|
||
|
||
try:
|
||
print(f"\n切割片段 {i+1}/{len(scenes)} ({transition_type}):")
|
||
print(f" 开始时间: {timedelta(seconds=start_time)}")
|
||
print(f" 结束时间: {timedelta(seconds=end_time)}")
|
||
print(f" 时长: {duration:.2f}秒")
|
||
print(f" 转场类型: {transition_type} (置信度: {scene['transition_confidence']:.2f})")
|
||
|
||
|
||
|
||
clips_info.append({
|
||
'index': i,
|
||
'file': output_file,
|
||
'start': start_time,
|
||
'end': end_time,
|
||
'duration': duration,
|
||
'transition_type': transition_type,
|
||
'confidence': scene['transition_confidence']
|
||
})
|
||
except Exception as e:
|
||
print(f" ✗ 切割失败: {str(e)}")
|
||
|
||
return clips_info
|
||
|
||
def generate_analysis_report(clips_info, output_dir):
|
||
"""
|
||
生成分析报告和可视化
|
||
"""
|
||
if not clips_info:
|
||
return
|
||
|
||
# 转场类型统计
|
||
transition_stats = {}
|
||
duration_by_type = {}
|
||
|
||
for clip in clips_info:
|
||
t_type = clip['transition_type']
|
||
transition_stats[t_type] = transition_stats.get(t_type, 0) + 1
|
||
if t_type not in duration_by_type:
|
||
duration_by_type[t_type] = []
|
||
duration_by_type[t_type].append(clip['duration'])
|
||
|
||
# 生成报告文件
|
||
report_file = os.path.join(output_dir, 'enhanced_analysis_report.txt')
|
||
with open(report_file, 'w', encoding='utf-8') as f:
|
||
f.write("增强视频切割分析报告\n")
|
||
f.write("=" * 50 + "\n\n")
|
||
|
||
f.write(f"\n详细片段信息:\n")
|
||
for clip in clips_info:
|
||
f.write(f"\"textIdx\":{clip['index']+1},\n")
|
||
f.write(f"\"time_start\":{clip["start"]},\n")
|
||
f.write(f"\"time_end\":{clip["end"]},\n")
|
||
f.write(f" 时长: {clip['duration']:.2f}秒\n")
|
||
|
||
print(f"已生成增强分析报告: {report_file}")
|
||
|
||
def process_video_enhanced(video_path, output_base_dir, sample_rate, method, threshold, ffmpeg_path):
|
||
"""
|
||
增强的视频处理函数
|
||
"""
|
||
video_filename = os.path.splitext(os.path.basename(video_path))[0]
|
||
video_output_dir = os.path.join(output_base_dir, video_filename)
|
||
|
||
if not os.path.exists(video_output_dir):
|
||
os.makedirs(video_output_dir)
|
||
|
||
frames_dir = os.path.join(video_output_dir, 'frames')
|
||
clips_dir = os.path.join(video_output_dir, 'clips')
|
||
|
||
for dir_path in [frames_dir, clips_dir]:
|
||
if not os.path.exists(dir_path):
|
||
os.makedirs(dir_path)
|
||
|
||
print("\n增强处理参数:")
|
||
print(f"输入视频: {os.path.abspath(video_path)}")
|
||
print(f"输出目录: {os.path.abspath(video_output_dir)}")
|
||
print(f"检测方法: {method} (增强版)")
|
||
print(f"滑动窗口大小: {WINDOW_SIZE}")
|
||
print("-" * 60)
|
||
|
||
try:
|
||
# 步骤1: 提取帧
|
||
print("\n步骤1: 正在提取视频帧...")
|
||
frames_info = extract_frames(video_path, frames_dir, sample_rate)
|
||
|
||
# 步骤2: 增强场景检测
|
||
print("\n步骤2: 正在进行增强场景检测...")
|
||
scenes = enhanced_scene_detection(frames_info, method, threshold)
|
||
|
||
if not scenes:
|
||
print("未检测到场景变化")
|
||
return False
|
||
|
||
|
||
print("\n增强处理完成!")
|
||
return True
|
||
|
||
|
||
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 main():
|
||
"""主函数 - 增强版"""
|
||
print("=" * 60)
|
||
print("智能视频切割工具 - 增强版 (支持复杂转场检测)")
|
||
print("=" * 60)
|
||
|
||
ffmpeg_path = find_ffmpeg()
|
||
if ffmpeg_path:
|
||
print(f"已找到ffmpeg: {ffmpeg_path}")
|
||
else:
|
||
print("警告: 未找到ffmpeg，视频切割功能将不可用")
|
||
|
||
video_files = get_video_files(INPUT_VIDEO_PATH)
|
||
|
||
if not video_files:
|
||
print(f"错误: 在 '{INPUT_VIDEO_PATH}' 中没有找到视频文件")
|
||
return
|
||
|
||
parent_folder_name = get_parent_folder_name(INPUT_VIDEO_PATH)
|
||
output_dir = os.path.join(OUTPUT_DIR, f"{parent_folder_name}_enhanced")
|
||
if not os.path.exists(output_dir):
|
||
os.makedirs(output_dir)
|
||
|
||
print(f"\n增强版输出目录: {output_dir}")
|
||
|
||
successful = 0
|
||
failed = 0
|
||
|
||
for i, video_path in enumerate(video_files):
|
||
print("\n" + "=" * 60)
|
||
print(f"正在处理视频 [{i+1}/{len(video_files)}]: {os.path.basename(video_path)}")
|
||
print("=" * 60)
|
||
|
||
success = process_video_enhanced(
|
||
video_path=video_path,
|
||
output_base_dir=output_dir,
|
||
sample_rate=SAMPLE_RATE,
|
||
method=METHOD,
|
||
threshold=THRESHOLD,
|
||
ffmpeg_path=ffmpeg_path
|
||
)
|
||
|
||
if success:
|
||
successful += 1
|
||
else:
|
||
failed += 1
|
||
|
||
print("\n" + "=" * 60)
|
||
print("增强版批量处理完成!")
|
||
print("=" * 60)
|
||
print(f"总共处理: {len(video_files)} 个视频文件")
|
||
print(f"成功: {successful} 个")
|
||
print(f"失败: {failed} 个")
|
||
print(f"输出目录: {os.path.abspath(output_dir)}")
|
||
|
||
if __name__ == "__main__":
|
||
main() |