83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
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from VideoSplitter import *
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import cv2
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from PIL import Image
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import matplotlib.pyplot as plt
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def get_pil_images_from_frame_numbers(video_path, frame_numbers):
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"""
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从视频中读取指定帧号的帧，并转换为PIL图像
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Args:
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video_path: 视频文件路径
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frame_numbers: 要读取的帧号列表
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Returns:
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pil_images: PIL图像列表
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"""
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cap = cv2.VideoCapture(video_path)
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fps = cap.get(cv2.CAP_PROP_FPS)
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pil_images = []
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print(f"开始读取 {len(frame_numbers)} 个指定帧并转换为PIL图像...")
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for i, frame_number in enumerate(frame_numbers):
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# 设置视频位置到指定帧
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cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
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ret, frame = cap.read()
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if ret:
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# 将OpenCV的BGR格式转换为RGB格式
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# 转换为PIL图像
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pil_image = Image.fromarray(frame_rgb)
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pil_images.append(pil_image)
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print(f"已读取并转换帧 {frame_number} ({i+1}/{len(frame_numbers)})")
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else:
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print(f"无法读取帧 {frame_number}")
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cap.release()
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print(f"完成! 共获得了 {len(pil_images)} 个PIL图像")
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return pil_images
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video_path = "/root/autodl-tmp/hot_video_analyse/source/sample_demo_1.mp4"
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frames_dir = "/root/autodl-tmp/hot_video_analyse/source/Splitter/hot_video_analyse/sample_demo_1/frames"
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output_dir = "/root/autodl-tmp/hot_video_analyse/source/pil_images"
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sample_rate = 1
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method = "ssim"
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threshold = 0.8
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# 步骤1: 提取帧
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print("\n步骤1: 正在提取视频帧...")
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frames_info = extract_frames(video_path, frames_dir, sample_rate)
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# 步骤2: 检测场景变化
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print("\n步骤2: 正在检测场景变化...")
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scenes, a = detect_scene_changes(frames_info, method, threshold)
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print(f"场景开始帧号: {a}")
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# 步骤3: 将场景开始帧转换为PIL图像
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print("\n步骤3: 转换场景开始帧为PIL图像...")
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pil_images = get_pil_images_from_frame_numbers(video_path, a)
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# 显示结果
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print(f"\n成功获得 {len(pil_images)} 个PIL图像")
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# 查看第一个PIL图像的信息
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if pil_images:
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first_pil = pil_images[0]
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pil_output_path = os.path.join(output_dir, f"first_scene_frame_{a[0]}.jpg")
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first_pil.save(pil_output_path, quality=95)
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print(f"第一个PIL图像信息:")
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print(f" 尺寸: {first_pil.size}")
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print(f" 模式: {first_pil.mode}")
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"""
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视频帧
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音频转文字
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全视频
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"""
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