48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import numpy as np
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from PIL import Image
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import torch
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import subprocess
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import json
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def tensor2pil(image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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def pil2tensor(image):
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def getVideoInfo(video_path):
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command = [
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'ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries',
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'stream=avg_frame_rate,duration,width,height', '-of', 'json', video_path
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]
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# 运行ffprobe命令
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result = subprocess.run(command, stderr=subprocess.PIPE, stdout=subprocess.PIPE)
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# 将输出转化为字符串
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output = result.stdout.decode('utf-8').strip()
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print(output)
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data = json.loads(output)
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# 查找视频流信息
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if 'streams' in data and len(data['streams']) > 0:
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stream = data['streams'][0] # 获取第一个视频流
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fps = stream.get('avg_frame_rate')
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if fps is not None:
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# 帧率可能是一个分数形式的字符串，例如 "30/1" 或 "20.233000"
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if '/' in fps:
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num, denom = map(int, fps.split('/'))
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fps = num / denom
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else:
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fps = float(fps) # 直接转换为浮点数
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width = int(stream.get('width'))
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height = int(stream.get('height'))
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duration = float(stream.get('duration'))
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return_data = {'fps': fps, 'width': width, 'height': height, 'duration': duration}
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else:
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return_data = {}
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return return_data
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def get_image_size(image_path):
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# 打开图像文件
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with Image.open(image_path) as img:
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# 获取图像的宽度和高度
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width, height = img.size
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return width, height |