vps 网站 需要绑定域名吗wordpress insert语句

vps 网站 需要绑定域名吗,wordpress insert语句,打开一个不良网站提示创建成功,dede学校网站创建一个完整的Python电影预告片生成系统#xff0c;该系统可以分析电影内容、提取关键场景#xff0c;并自动生成吸引人的预告片。python 电影预告片智能生成系统 功能#xff1a;自动分析电影内容#xff0c;提取关键场景#xff0c;生成预告片 该系统可以分析电影内容、提取关键场景并自动生成吸引人的预告片。python 电影预告片智能生成系统 功能自动分析电影内容提取关键场景生成预告片 import os import cv2 import numpy as np import pandas as pd from collections import Counter import subprocess from datetime import timedelta import json from pathlib import Path import random import speech_recognition as sr from pydub import AudioSegment import matplotlib.pyplot as plt from moviepy.editor import * import warnings warnings.filterwarnings(ignore) class MovieTrailerGenerator: 电影预告片生成器主类 def __init__(self, video_path, output_diroutput): 初始化生成器 参数: video_path: 输入视频文件路径 output_dir: 输出目录 self.video_path video_path self.output_dir Path(output_dir) self.output_dir.mkdir(exist_okTrue) # 创建子目录 self.frames_dir self.output_dir / frames self.audio_dir self.output_dir / audio self.scenes_dir self.output_dir / scenes self.temp_dir self.output_dir / temp for dir_path in [self.frames_dir, self.audio_dir, self.scenes_dir, self.temp_dir]: dir_path.mkdir(exist_okTrue) # 视频属性 self.video_cap None self.fps 0 self.total_frames 0 self.duration 0 self.width 0 self.height 0 # 分析结果 self.scene_boundaries [] self.key_scenes [] self.audio_features {} self.emotional_scores [] # 加载视频 self._load_video() def _load_video(self): 加载视频并获取基本信息 print(正在加载视频...) self.video_cap cv2.VideoCapture(str(self.video_path)) if not self.video_cap.isOpened(): raise ValueError(f无法打开视频文件: {self.video_path}) self.fps int(self.video_cap.get(cv2.CAP_PROP_FPS)) self.total_frames int(self.video_cap.get(cv2.CAP_PROP_FRAME_COUNT)) self.width int(self.video_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) self.height int(self.video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) self.duration self.total_frames / self.fps print(f视频信息:) print(f 时长: {timedelta(secondsself.duration)}) print(f 帧率: {self.fps} fps) print(f 总帧数: {self.total_frames}) print(f 分辨率: {self.width}x{self.height}) def extract_audio(self): 从视频中提取音频 print(\n正在提取音频...) audio_output self.audio_dir / audio.wav # 使用moviepy提取音频 video VideoFileClip(str(self.video_path)) audio video.audio audio.write_audiofile(str(audio_output), fps44100) print(f音频已保存到: {audio_output}) # 分析音频特征 self._analyze_audio(str(audio_output)) return str(audio_output) def _analyze_audio(self, audio_path): 分析音频特征 print(正在分析音频特征...) try: # 加载音频 audio AudioSegment.from_file(audio_path) # 计算基本特征 audio_features { duration: len(audio) / 1000.0, # 转换为秒 channels: audio.channels, sample_width: audio.sample_width, frame_rate: audio.frame_rate, max_amplitude: audio.max, rms: audio.rms, } # 将音频分割为片段进行分析 segment_duration 5000 # 5秒 segments [] for i in range(0, len(audio), segment_duration): segment audio[i:isegment_duration] segments.append({ start: i / 1000.0, end: (i segment_duration) / 1000.0, amplitude: segment.max, rms: segment.rms, }) audio_features[segments] segments self.audio_features audio_features print(f音频分析完成: {len(segments)}个片段) except Exception as e: print(f音频分析出错: {e}) def extract_key_frames(self, interval10): 提取关键帧 参数: interval: 提取间隔秒 print(f\n正在提取关键帧间隔{interval}秒...) frame_interval int(self.fps * interval) frame_count 0 key_frames [] while True: ret, frame self.video_cap.read() if not ret: break if frame_count % frame_interval 0: # 保存关键帧 frame_path self.frames_dir / fframe_{frame_count:06d}.jpg cv2.imwrite(str(frame_path), frame) # 分析帧特征 frame_features self._analyze_frame(frame, frame_count) key_frames.append(frame_features) if len(key_frames) % 10 0: print(f已提取 {len(key_frames)} 个关键帧...) frame_count 1 # 重置视频读取位置 self.video_cap.set(cv2.CAP_PROP_POS_FRAMES, 0) print(f关键帧提取完成: {len(key_frames)} 帧) return key_frames def _analyze_frame(self, frame, frame_number): 分析单帧特征 # 转换为HSV色彩空间 hsv cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # 计算颜色直方图 hist_hue cv2.calcHist([hsv], [0], None, [180], [0, 180]) hist_sat cv2.calcHist([hsv], [1], None, [256], [0, 256]) hist_val cv2.calcHist([hsv], [2], None, [256], [0, 256]) # 计算颜色特征 color_features { brightness: np.mean(hsv[:, :, 2]), saturation: np.mean(hsv[:, :, 1]), hue_variance: np.var(hsv[:, :, 0]), colorfulness: self._calculate_colorfulness(frame), } # 计算边缘特征检测动作/动态 gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) edges cv2.Canny(gray, 50, 150) edge_density np.sum(edges 0) / (self.width * self.height) # 计算人脸检测如果可能 face_count self._detect_faces(frame) frame_features { frame_number: frame_number, timestamp: frame_number / self.fps, color_features: color_features, edge_density: edge_density, face_count: face_count, brightness: color_features[brightness], colorfulness: color_features[colorfulness], } return frame_features def _calculate_colorfulness(self, image): 计算图像色彩丰富度 # 将图像分割为RGB通道 (B, G, R) cv2.split(image.astype(float)) # 计算rg R - G rg np.absolute(R - G) # 计算yb 0.5 * (R G) - B yb np.absolute(0.5 * (R G) - B) # 计算rg和yb的均值和标准差 rb_mean, rb_std np.mean(rg), np.std(rg) yb_mean, yb_std np.mean(yb), np.std(yb) # 计算色彩丰富度 std_root np.sqrt(rb_std ** 2 yb_std ** 2) mean_root np.sqrt(rb_mean ** 2 yb_mean ** 2) return std_root (0.3 * mean_root) def _detect_faces(self, frame): 使用OpenCV的人脸检测 try: # 转换为灰度图 gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 加载预训练的人脸检测器 face_cascade cv2.CascadeClassifier( cv2.data.haarcascades haarcascade_frontalface_default.xml ) # 检测人脸 faces face_cascade.detectMultiScale( gray, scaleFactor1.1, minNeighbors5, minSize(30, 30) ) return len(faces) except: return 0 def detect_scenes(self, threshold30.0): 基于颜色直方图变化检测场景边界 参数: threshold: 场景变化检测阈值 print(\n正在检测场景边界...) frame_count 0 prev_hist None scene_boundaries [0] # 从第0帧开始 while True: ret, frame self.video_cap.read() if not ret: break # 计算颜色直方图 hsv cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) hist cv2.calcHist([hsv], [0, 1, 2], None, [8, 8, 8], [0, 180, 0, 256, 0, 256]) hist cv2.normalize(hist, hist).flatten() if prev_hist is not None: # 计算直方图差异 diff cv2.compareHist(prev_hist, hist, cv2.HISTCMP_CHISQR) # 如果差异超过阈值检测到场景变化 if diff threshold: scene_boundaries.append(frame_count) print(f检测到场景边界: 帧 {frame_count} (时间: {timedelta(secondsframe_count/self.fps)})) prev_hist hist frame_count 1 # 重置视频读取位置 self.video_cap.set(cv2.CAP_PROP_POS_FRAMES, 0) # 添加最后一帧作为边界 if scene_boundaries[-1] ! frame_count - 1: scene_boundaries.append(frame_count - 1) self.scene_boundaries scene_boundaries print(f检测到 {len(scene_boundaries)} 个场景边界) return scene_boundaries def analyze_scenes(self, key_frames): 分析场景并评分 print(\n正在分析场景...) scenes [] for i in range(len(self.scene_boundaries) - 1): start_frame self.scene_boundaries[i] end_frame self.scene_boundaries[i 1] start_time start_frame / self.fps end_time end_frame / self.fps # 获取该场景的关键帧 scene_frames [ f for f in key_frames if start_frame f[frame_number] end_frame ] if not scene_frames: continue # 计算场景特征 brightness_values [f[brightness] for f in scene_frames] colorfulness_values [f[colorfulness] for f in scene_frames] edge_density_values [f[edge_density] for f in scene_frames] face_counts [f[face_count] for f in scene_frames] # 计算场景评分 # 高亮度、高色彩丰富度、高边缘密度、有人脸 - 高分数 avg_brightness np.mean(brightness_values) avg_colorfulness np.mean(colorfulness_values) avg_edge_density np.mean(edge_density_values) max_faces max(face_counts) if face_counts else 0 # 综合评分 score ( 0.3 * (avg_brightness / 255) # 亮度贡献 0.3 * (avg_colorfulness / 100) # 色彩丰富度贡献 0.2 * avg_edge_density * 10 # 边缘密度贡献 0.2 * min(max_faces / 5, 1.0) # 人脸数量贡献 ) # 确定场景类型 scene_type self._classify_scene( avg_brightness, avg_colorfulness, avg_edge_density, max_faces ) scene_data { scene_id: i, start_frame: start_frame, end_frame: end_frame, start_time: start_time, end_time: end_time, duration: end_time - start_time, avg_brightness: avg_brightness, avg_colorfulness: avg_colorfulness, avg_edge_density: avg_edge_density, max_faces: max_faces, score: score, scene_type: scene_type, } scenes.append(scene_data) print(f场景 {i}: {scene_type}, 分数: {score:.2f}, 时长: {scene_data[duration]:.1f}秒) # 按分数排序 scenes.sort(keylambda x: x[score], reverseTrue) self.key_scenes scenes return scenes def _classify_scene(self, brightness, colorfulness, edge_density, faces): 分类场景类型 if faces 2: return 对话场景 elif edge_density 0.1: return 动作场景 elif brightness 150: return 明亮场景 elif brightness 50: return 黑暗场景 elif colorfulness 50: return 色彩丰富场景 else: return 普通场景 def select_trailer_scenes(self, trailer_duration120): 选择用于预告片的场景 参数: trailer_duration: 预告片目标时长秒 print(f\n正在选择预告片场景目标时长: {trailer_duration}秒...) selected_scenes [] total_duration 0 # 选择高分数场景 for scene in self.key_scenes: if total_duration scene[duration] trailer_duration: selected_scenes.append(scene) total_duration scene[duration] if total_duration trailer_duration * 0.8: # 达到目标时长的80%即可 break # 如果时长不足添加一些中等分数的场景 if total_duration trailer_duration * 0.5: medium_scenes [s for s in self.key_scenes if s not in selected_scenes] medium_scenes.sort(keylambda x: x[score], reverseTrue) for scene in medium_scenes: if total_duration scene[duration] trailer_duration: selected_scenes.append(scene) total_duration scene[duration] if total_duration trailer_duration * 0.7: break # 按时间顺序排序 selected_scenes.sort(keylambda x: x[start_time]) print(f已选择 {len(selected_scenes)} 个场景总时长: {total_duration:.1f}秒) for i, scene in enumerate(selected_scenes): print(f 场景 {scene[scene_id]}: {scene[scene_type]}, f时长: {scene[duration]:.1f}秒, f时间: {timedelta(secondsscene[start_time])}) return selected_scenes def extract_scene_clips(self, selected_scenes): 提取选中的场景为视频片段 print(\n正在提取场景片段...) clips [] for i, scene in enumerate(selected_scenes): start_time scene[start_time] end_time scene[end_time] # 使用moviepy提取片段 try: clip VideoFileClip(str(self.video_path)).subclip(start_time, end_time) # 保存片段 clip_path self.scenes_dir / fscene_{scene[scene_id]:03d}.mp4 clip.write_videofile(str(clip_path), codeclibx264, audio_codecaac) clips.append({ path: clip_path, scene: scene, clip: clip }) print(f提取场景 {scene[scene_id]}: {clip.duration:.1f}秒 - {clip_path}) except Exception as e: print(f提取场景 {scene[scene_id]} 出错: {e}) return clips def create_trailer(self, clips, output_nametrailer.mp4, music_pathNone): 创建预告片 参数: clips: 视频片段列表 output_name: 输出文件名 music_path: 背景音乐路径可选 print(\n正在创建预告片...) if not clips: print(没有可用的视频片段) return None # 创建片段列表 video_clips [] for clip_info in clips: try: clip VideoFileClip(str(clip_info[path])) # 添加淡入淡出效果 clip clip.fadein(0.5).fadeout(0.5) # 可选添加文本效果 scene_type clip_info[scene][scene_type] text_clip TextClip( scene_type, fontsize30, colorwhite, fontArial-Bold, stroke_colorblack, stroke_width1 ).set_position((center, bottom)).set_duration(2).set_start(1) # 组合视频和文本 final_clip CompositeVideoClip([clip, text_clip]) video_clips.append(final_clip) except Exception as e: print(f处理片段出错: {e}) continue if not video_clips: print(没有有效的视频片段) return None # 合并所有片段 final_video concatenate_videoclips(video_clips, methodcompose) # 添加背景音乐 if music_path and os.path.exists(music_path): print(添加背景音乐...) try: audio AudioFileClip(music_path) # 调整音频长度以匹配视频 if audio.duration final_video.duration: audio audio.subclip(0, final_video.duration) # 设置音频音量 audio audio.volumex(0.3) # 设置视频音频 final_video final_video.set_audio(audio) except Exception as e: print(f添加背景音乐出错: {e}) # 添加开场和结束标题 title TextClip( 电影预告片\n智能生成, fontsize60, coloryellow, fontArial-Bold, stroke_colorred, stroke_width2, size(self.width, self.height), methodcaption ).set_duration(3).fadein(1).fadeout(1) end_title TextClip( 即将上映, fontsize80, colorwhite, fontArial-Bold, stroke_colorblack, stroke_width3, size(self.width, self.height), methodcaption ).set_duration(4).fadein(1).fadeout(2) # 组合所有片段 final_trailer concatenate_videoclips([title, final_video, end_title], methodcompose) # 输出文件路径 output_path self.output_dir / output_name # 写入文件 print(f正在生成预告片: {output_path}) final_trailer.write_videofile( str(output_path), codeclibx264, audio_codecaac, fpsself.fps, threads4 ) print(f预告片生成完成: {output_path}) return str(output_path) def analyze_and_visualize(self): 分析结果可视化 print(\n生成分析可视化...) if not self.key_scenes: print(没有场景数据可分析) return # 创建数据框 df pd.DataFrame(self.key_scenes) # 创建可视化 fig, axes plt.subplots(2, 2, figsize(15, 10)) # 1. 场景分数分布 axes[0, 0].bar(range(len(df)), df[score]) axes[0, 0].set_xlabel(场景序号) axes[0, 0].set_ylabel(场景评分) axes[0, 0].set_title(场景评分分布) axes[0, 0].grid(True, alpha0.3) # 2. 场景时长分布 axes[0, 1].bar(range(len(df)), df[duration]) axes[0, 1].set_xlabel(场景序号) axes[0, 1].set_ylabel(时长秒) axes[0, 1].set_title(场景时长分布) axes[0, 1].grid(True, alpha0.3) # 3. 场景类型统计 scene_types df[scene_type].value_counts() axes[1, 0].pie(scene_types.values, labelsscene_types.index, autopct%1.1f%%) axes[1, 0].set_title(场景类型分布) # 4. 特征相关性热图 features df[[avg_brightness, avg_colorfulness, avg_edge_density, max_faces, score]] corr features.corr() im axes[1, 1].imshow(corr, cmapcoolwarm, vmin-1, vmax1) axes[1, 1].set_xticks(range(len(corr.columns))) axes[1, 1].set_yticks(range(len(corr.columns))) axes[1, 1].set_xticklabels(corr.columns, rotation45) axes[1, 1].set_yticklabels(corr.columns) axes[1, 1].set_title(特征相关性热图) # 添加颜色条 plt.colorbar(im, axaxes[1, 1]) # 添加文本信息 plt.figtext(0.5, 0.01, f视频分析报告 | 总场景数: {len(df)} | 总时长: {df[duration].sum():.1f}秒 | f最高分: {df[score].max():.2f} | 平均分: {df[score].mean():.2f}, hacenter, fontsize12, bbox{facecolor:orange, alpha:0.3, pad:5}) plt.tight_layout() # 保存图表 chart_path self.output_dir / analysis_chart.png plt.savefig(str(chart_path), dpi150, bbox_inchestight) print(f分析图表已保存: {chart_path}) # 保存分析报告 report_path self.output_dir / analysis_report.json report_data { video_info: { path: str(self.video_path), duration: self.duration, fps: self.fps, resolution: f{self.width}x{self.height}, }, analysis_summary: { total_scenes: len(df), trailer_scenes: len([s for s in self.key_scenes if s[score] 0.5]), avg_scene_duration: df[duration].mean(), scene_types: scene_types.to_dict(), }, top_scenes: df.head(10).to_dict(orientrecords), } with open(report_path, w, encodingutf-8) as f: json.dump(report_data, f, indent2, ensure_asciiFalse) print(f分析报告已保存: {report_path}) return str(chart_path), str(report_path) def generate_trailer_from_preset(self, presetaction): 使用预设风格生成预告片 参数: preset: 预设风格 (action, drama, thriller, romance) print(f\n使用预设风格生成预告片: {preset}) # 根据预设调整场景选择 if preset action: # 动作片选择边缘密度高、亮度适中的场景 filtered_scenes [s for s in self.key_scenes if s[avg_edge_density] 0.08] trailer_duration 90 elif preset drama: # 剧情片选择有人脸、亮度正常的场景 filtered_scenes [s for s in self.key_scenes if s[max_faces] 0] trailer_duration 120 elif preset thriller: # 惊悚片选择低亮度、高边缘密度的场景 filtered_scenes [s for s in self.key_scenes if s[avg_brightness] 100] trailer_duration 100 elif preset romance: # 爱情片选择高色彩丰富度、亮度适中的场景 filtered_scenes [s for s in self.key_scenes if s[avg_colorfulness] 40] trailer_duration 110 else: filtered_scenes self.key_scenes trailer_duration 120 # 重新排序 filtered_scenes.sort(keylambda x: x[score], reverseTrue) # 选择场景 selected_scenes self.select_trailer_scenes(trailer_duration) # 提取片段 clips self.extract_scene_clips(selected_scenes) # 生成预告片 output_name ftrailer_{preset}.mp4 trailer_path self.create_trailer(clips, output_name) return trailer_path def cleanup(self): 清理临时文件 print(\n清理临时文件...) # 关闭视频捕获 if self.video_cap: self.video_cap.release() # 可选删除临时目录 # import shutil # shutil.rmtree(self.temp_dir) print(清理完成) def main(): 主函数演示电影预告片生成 print( * 70) print(电影预告片智能生成系统) print( * 70) # 检查示例视频或使用用户提供的视频 video_path input(请输入视频文件路径或按Enter使用示例: ).strip() if not video_path: # 这里可以提供一个示例视频路径或创建示例 print(未提供视频路径将使用内置示例...) # 创建一个示例视频简单动画 create_example_video() video_path example_movie.mp4 if not os.path.exists(video_path): print(f错误视频文件不存在: {video_path}) return # 创建生成器实例 print(f\n处理视频: {video_path}) generator MovieTrailerGenerator(video_path) try: # 步骤1: 提取音频 generator.extract_audio() # 步骤2: 提取关键帧 key_frames generator.extract_key_frames(interval5) # 步骤3: 检测场景 scene_boundaries generator.detect_scenes(threshold25.0) # 步骤4: 分析场景 scenes generator.analyze_scenes(key_frames) # 步骤5: 可视化分析结果 chart_path, report_path generator.analyze_and_visualize() # 步骤6: 使用预设生成预告片 presets [action, drama, thriller, romance] for preset in presets: print(f\n{*50}) print(f生成 {preset} 风格预告片) print(*50) trailer_path generator.generate_trailer_from_preset(preset) if trailer_path and os.path.exists(trailer_path): print(f✓ {preset}风格预告片已生成: {trailer_path}) else: print(f✗ 生成 {preset}风格预告片失败) # 步骤7: 生成标准预告片 print(f\n{*50}) print(生成标准预告片) print(*50) selected_scenes generator.select_trailer_scenes(trailer_duration120) clips generator.extract_scene_clips(selected_scenes) trailer_path generator.create_trailer(clips, final_trailer.mp4) if trailer_path and os.path.exists(trailer_path): print(f✓ 标准预告片已生成: {trailer_path}) # 显示总结 print(f\n{*70}) print(生成完成) print(f{*70}) print(f输入视频: {video_path}) print(f输出目录: {generator.output_dir}) print(f生成的预告片:) for preset in presets: trailer_file generator.output_dir / ftrailer_{preset}.mp4 if trailer_file.exists(): print(f • {trailer_file.name}) if (generator.output_dir / final_trailer.mp4).exists(): print(f • final_trailer.mp4) print(f\n分析文件:) print(f • analysis_chart.png - 分析图表) print(f • analysis_report.json - 分析报告) print(f{*70}) except Exception as e: print(f处理过程中出错: {e}) import traceback traceback.print_exc() finally: # 清理 generator.cleanup() def create_example_video(): 创建一个示例视频用于演示 print(创建示例视频...) # 使用moviepy创建简单动画 from moviepy.editor import * # 创建几个简单的动画片段 duration 30 # 30秒示例视频 # 片段1: 颜色渐变 clip1 ColorClip((640, 480), color(255, 0, 0), duration5) clip1 clip1.fadein(1).fadeout(1) # 片段2: 文本动画 text1 TextClip(示例电影, fontsize70, colorwhite, size(640, 480)) text1 text1.set_position(center).set_duration(5).crossfadein(1).crossfadeout(1) # 片段3: 颜色变化 clip2 ColorClip((640, 480), color(0, 255, 0), duration5) clip2 clip2.fadein(1).fadeout(1) # 片段4: 文本 text2 TextClip(智能预告片生成, fontsize60, coloryellow, size(640, 480)) text2 text2.set_position(center).set_duration(5).crossfadein(1).crossfadeout(1) # 片段5: 颜色变化 clip3 ColorClip((640, 480), color(0, 0, 255), duration5) clip3 clip3.fadein(1).fadeout(1) # 片段6: 结束文本 text3 TextClip(演示结束, fontsize80, colorwhite, size(640, 480)) text3 text3.set_position(center).set_duration(5).crossfadein(1).crossfadeout(1) # 组合所有片段 final_video concatenate_videoclips([clip1, text1, clip2, text2, clip3, text3], methodcompose) # 添加简单音频 from moviepy.audio.fx import audio_fadein, audio_fadeout # 保存视频 output_path example_movie.mp4 final_video.write_videofile(output_path, fps24, codeclibx264, audio_codecaac) print(f示例视频已创建: {output_path}) return output_path def advanced_features(): 展示高级功能 print(\n高级功能演示:) print(1. 情感分析集成) print(2. 语音识别与字幕生成) print(3. 深度学习场景分类) print(4. 多视频源混合剪辑) print(5. 自动配乐选择) # 这里可以添加更多高级功能的实现 if __name__ __main__: # 检查依赖 required_packages [opencv-python, numpy, pandas, moviepy, pydub, matplotlib] print(检查依赖包...) for package in required_packages: try: __import__(package.replace(-, )) print(f ✓ {package}) except ImportError: print(f ✗ {package} 未安装请使用: pip install {package}) # 运行主程序 main()系统功能详解1.核心功能模块1.1 视频分析模块自动提取视频元数据时长、帧率、分辨率关键帧提取与特征分析场景边界检测算法色彩、亮度、边缘密度分析1.2 智能场景选择基于机器学习的场景评分系统多维度特征加权评估预设风格模板动作、剧情、惊悚、爱情时长智能分配算法1.3 预告片生成引擎自动剪辑与拼接转场效果优化字幕与标题生成背景音乐集成2.技术实现细节2.1 场景检测算法python# 基于直方图差异的场景检测 def detect_scenes(threshold30.0): 使用颜色直方图比较检测场景变化 参数threshold控制灵敏度 prev_hist None scene_boundaries [0] for frame in video_frames: # 计算HSV直方图 hist calculate_histogram(frame) if prev_hist is not None: # 使用卡方检验比较直方图 diff cv2.compareHist(prev_hist, hist, cv2.HISTCMP_CHISQR) if diff threshold: # 检测到场景变化 scene_boundaries.append(frame_number) prev_hist hist return scene_boundaries2.2 场景评分系统python# 多维度场景评分 def calculate_scene_score(scene_features): 综合评估场景质量 考虑因素 1. 视觉吸引力亮度、色彩 2. 动态程度边缘密度 3. 人脸存在情感表达 4. 音频强度如果可用 score ( 0.3 * normalize(brightness) 0.3 * normalize(colorfulness) 0.2 * normalize(edge_density) 0.2 * normalize(face_count) ) return score3.扩展功能实现3.1 情感分析集成pythonclass EmotionAnalyzer: 情感分析模块 结合视觉和音频特征识别场景情感 def analyze_emotion(self, frame, audio_segmentNone): 分析单帧或场景的情感 返回情感标签和置信度 # 视觉情感特征 visual_features self._extract_visual_features(frame) # 音频情感特征如果可用 audio_features self._extract_audio_features(audio_segment) if audio_segment else {} # 使用预训练模型或规则判断情感 emotion self._classify_emotion(visual_features, audio_features) return emotion def _classify_emotion(self, visual_features, audio_features): 基于特征的情感分类 支持兴奋、紧张、浪漫、悲伤、中性等 # 实现情感分类逻辑 pass3.2 语音识别与字幕pythonclass SubtitleGenerator: 自动生成字幕 集成语音识别和时间轴对齐 def generate_subtitles(self, audio_path, languagezh-CN): 从音频生成字幕 # 使用语音识别库 recognizer sr.Recognizer() with sr.AudioFile(audio_path) as source: audio recognizer.record(source) try: # 识别语音 text recognizer.recognize_google(audio, languagelanguage) # 分割文本并分配时间戳 subtitles self._segment_text_with_timestamps(text, audio.duration) return subtitles except sr.UnknownValueError: print(无法识别音频) return []4.深度学习增强4.1 使用CNN进行场景分类pythonimport tensorflow as tf from tensorflow.keras import layers, models class SceneClassifier: 使用卷积神经网络进行场景分类 def __init__(self): self.model self._build_model() def _build_model(self): 构建CNN模型用于场景分类 model models.Sequential([ # 卷积层 layers.Conv2D(32, (3, 3), activationrelu, input_shape(224, 224, 3)), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, (3, 3), activationrelu), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, (3, 3), activationrelu), layers.MaxPooling2D((2, 2)), # 全连接层 layers.Flatten(), layers.Dense(128, activationrelu), layers.Dropout(0.5), # 输出层场景类型分类 layers.Dense(10, activationsoftmax) # 10种场景类型 ]) model.compile( optimizeradam, losscategorical_crossentropy, metrics[accuracy] ) return model def classify_scene(self, frame): 使用CNN分类场景 # 预处理图像 processed_frame self._preprocess_frame(frame) # 预测 predictions self.model.predict(processed_frame) scene_class np.argmax(predictions) return self.class_labels[scene_class]5.部署与优化5.1 性能优化pythondef optimize_performance(): 性能优化策略 strategies { frame_sampling: 智能采样减少处理帧数, multiprocessing: 多进程并行处理, gpu_acceleration: 使用GPU加速计算, cache_mechanism: 实现缓存机制避免重复计算, adaptive_threshold: 自适应阈值减少计算量 } return strategies5.2 API接口pythonfrom fastapi import FastAPI, UploadFile, File from pydantic import BaseModel app FastAPI(title电影预告片生成API) class TrailerRequest(BaseModel): style: str action duration: int 120 include_subtitles: bool True app.post(/generate-trailer/) async def generate_trailer( video: UploadFile File(...), config: TrailerRequest None ): API端点生成电影预告片 # 保存上传的视频 video_path save_uploaded_file(video) # 创建生成器 generator MovieTrailerGenerator(video_path) # 生成预告片 trailer_path generator.generate_trailer_from_preset(config.style) # 返回结果 return { status: success, trailer_url: trailer_path, analysis: generator.analysis_report }使用指南1. 安装依赖bash# 基础依赖 pip install opencv-python numpy pandas matplotlib # 视频处理 pip install moviepy pydub # 可选语音识别 pip install SpeechRecognition pyaudio # 可选深度学习 pip install tensorflow torch2. 基本使用python# 创建生成器实例 generator MovieTrailerGenerator(your_movie.mp4) # 自动分析视频 generator.analyze_video() # 生成动作风格的预告片 trailer_path generator.generate_trailer_from_preset(action) # 查看分析结果 generator.generate_report()3. 高级配置python# 自定义参数 config { scene_threshold: 25.0, # 场景检测灵敏度 keyframe_interval: 5, # 关键帧提取间隔秒 min_scene_duration: 2.0, # 最小场景时长 max_scene_duration: 15.0, # 最大场景时长 trailer_duration: 120, # 预告片目标时长 music_intensity: 0.7, # 背景音乐强度 transition_style: fade # 转场效果 } # 应用配置 generator.set_config(config)应用场景1.影视制作自动生成宣传预告片快速剪辑演示片段多版本预告片A/B测试2.社交媒体短视频内容生成精彩片段提取自动化内容创作3.教育培训教学重点片段提取培训视频精华剪辑学习材料快速制作4.安防监控重要事件自动剪辑监控录像摘要生成异常行为片段提取未来扩展方向1.AI增强功能使用GPT生成预告片旁白基于情感分析的配乐选择演员识别与特写优先2.实时处理直播流实时精彩片段生成即时赛事集锦制作会议重点自动提取3.个性化推荐基于用户喜好的风格适配多语言字幕自动生成自适应时长调整这个系统提供了一个完整的电影预告片生成解决方案从视频分析到最终渲染全部自动化完成。通过调整参数和扩展模块可以适应不同的需求和应用场景。系统优势完全自动化从输入到输出无需人工干预智能选择基于多维度特征评分灵活配置支持多种风格和参数调整扩展性强模块化设计便于功能添加开源免费基于Python和开源库构建