用手机可以做网站,北京最大的广告制作公司,WordPress自动搜索文章插件,苏州外贸网站解码并处理视频流的多线程应用 随着视频处理技术的不断发展#xff0c;越来越多的应用需要对视频流进行解码和处理。在本文中#xff0c;我们将介绍一个基于Python的多线程应用程序#xff0c;该应用程序可以解码并处理多个RTSP视频流#xff0c;同时利用GPU加速#xff0…解码并处理视频流的多线程应用 随着视频处理技术的不断发展越来越多的应用需要对视频流进行解码和处理。在本文中我们将介绍一个基于Python的多线程应用程序该应用程序可以解码并处理多个RTSP视频流同时利用GPU加速以提高处理速度。
这个应用程序使用了一些关键的Python库和工具包括PyNvCodec、OpenCV、和PyCUDA等。它充分利用了现代GPU的计算能力实现了高效的视频解码和处理。 多线程解码 在这个应用程序中我们使用了Python的concurrent.futures库来实现多线程解码。每个视频流都在独立的线程中解码这样可以同时处理多个视频流充分利用了多核CPU的性能。
from concurrent.futures import ThreadPoolExecutor# ...# 创建线程池
pool ThreadPoolExecutor(max_workerslen(urls))
futures []# 遍历每个视频流并提交解码任务
for url in urls:future pool.submit(decode_rtsp_stream, index, url, gpuID)futures.append(future)index 1# 等待所有任务完成
pool.shutdown()# 获取每个任务的结果
for future in futures:future.result()视频解码和处理 视频解码是这个应用程序的核心功能。我们使用PyNvCodec库来进行视频解码同时利用了GPU来加速处理。
def decode_rtsp_stream(thread_index: int, url: str, gpu_id: int):# 获取视频流参数params get_stream_params(url)# ...# 创建NvDecoder实例nvdec nvc.PyNvDecoder(w, h, f, c, g)# ...while True:# 读取视频流数据bits proc.stdout.read(read_size)# ...# 解码视频帧surf nvdec.DecodeSurfaceFromPacket(enc_packet, pkt_data)# ...# 执行颜色空间转换和表面下载cvtSurface nv_cvt.Execute(surf, cc_ctx)success nv_down.DownloadSingleSurface(cvtSurface, data)# ...# 显示解码后的帧cv2.imshow(str(thread_index), new_data)cv2.waitKey(1)# ...完整代码 这个应用程序可以广泛用于视频监控、实时视频分析、视频编码和解码等领域。通过多线程解码和GPU加速它可以处理多个高分辨率视频流并在实时性要求较高的情况下提供流畅的显示和处理效果。
import os
import sys
import subprocess
import json
import PyNvCodec as nvc
import numpy as np
from io import BytesIO
from multiprocessing import Process
import uuid
import time
from concurrent.futures import ThreadPoolExecutor
import cv2
import pycuda.gpuarray as gpuarray
# import PytorchNvCodec as pnvc
import torch
import torchvision.transforms as Tdef add_cuda_dll_directories():if os.name nt:cuda_path os.environ.get(CUDA_PATH)if cuda_path:os.add_dll_directory(cuda_path)else:print(CUDA_PATH environment variable is not set., filesys.stderr)exit(1)sys_path os.environ.get(PATH)if sys_path:paths sys_path.split(;)for path in paths:if os.path.isdir(path) and path ! .:os.add_dll_directory(path)else:print(PATH environment variable is not set., filesys.stderr)exit(1)def surface_to_tensor(surface: nvc.Surface) - torch.Tensor:Converts planar rgb surface to cuda float tensor.if surface.Format() ! nvc.PixelFormat.RGB_PLANAR:raise RuntimeError(Surface shall be of RGB_PLANAR pixel format)surf_plane surface.PlanePtr()img_tensor pnvc.DptrToTensor(surf_plane.GpuMem(),surf_plane.Width(),surf_plane.Height(),surf_plane.Pitch(),surf_plane.ElemSize(),)if img_tensor is None:raise RuntimeError(Can not export to tensor.)img_tensor.resize_(3, int(surf_plane.Height() / 3), surf_plane.Width())img_tensor img_tensor.type(dtypetorch.cuda.FloatTensor)img_tensor torch.divide(img_tensor, 255.0)img_tensor torch.clamp(img_tensor, 0.0, 1.0)return img_tensordef get_stream_params(url: str):cmd [ffprobe,-v,quiet,-print_format,json,-show_format,-show_streams,url,]proc subprocess.Popen(cmd, stdoutsubprocess.PIPE)stdout proc.communicate()[0]bio BytesIO(stdout)json_out json.load(bio)params {}if not streams in json_out:return {}for stream in json_out[streams]:if stream[codec_type] video:params[width] stream[width]params[height] stream[height]params[framerate] float(eval(stream[avg_frame_rate]))codec_name stream[codec_name]is_h264 True if codec_name h264 else Falseis_hevc True if codec_name hevc else Falseif not is_h264 and not is_hevc:raise ValueError(Unsupported codec: codec_name . Only H.264 and HEVC are supported in this sample.)else:params[codec] (nvc.CudaVideoCodec.H264 if is_h264 else nvc.CudaVideoCodec.HEVC)pix_fmt stream[pix_fmt]is_yuv420 pix_fmt yuv420pis_yuv444 pix_fmt yuv444p# YUVJ420P and YUVJ444P are deprecated but still wide spread, so handle# them as well. They also indicate JPEG color range.is_yuvj420 pix_fmt yuvj420pis_yuvj444 pix_fmt yuvj444pif is_yuvj420:is_yuv420 Trueparams[color_range] nvc.ColorRange.JPEGif is_yuvj444:is_yuv444 Trueparams[color_range] nvc.ColorRange.JPEGif not is_yuv420 and not is_yuv444:raise ValueError(Unsupported pixel format: pix_fmt . Only YUV420 and YUV444 are supported in this sample.)else:params[format] (nvc.PixelFormat.NV12 if is_yuv420 else nvc.PixelFormat.YUV444)# Color range default option. We may have set when parsing# pixel format, so check first.if color_range not in params:params[color_range] nvc.ColorRange.MPEG# Check actual value.if color_range in stream:color_range stream[color_range]if color_range pc or color_range jpeg:params[color_range] nvc.ColorRange.JPEG# Color space default option:params[color_space] nvc.ColorSpace.BT_601# Check actual value.if color_space in stream:color_space stream[color_space]if color_space bt709:params[color_space] nvc.ColorSpace.BT_709return paramsreturn {}def decode_rtsp_stream(thread_index: int, url: str, gpu_id: int):params get_stream_params(url)if not len(params):raise ValueError(Can not get url streams params)w params[width]h params[height]f params[format]c params[codec]framerate params[framerate]g gpu_idif nvc.CudaVideoCodec.H264 c:codec_name h264elif nvc.CudaVideoCodec.HEVC c:codec_name hevcbsf_name codec_name _mp4toannexb,dump_extraallcmd [ffmpeg,-hide_banner,-i,url,-c:v,copy,-bsf:v,bsf_name,-f,codec_name,pipe:1,]proc subprocess.Popen(cmd, stdoutsubprocess.PIPE)nvdec nvc.PyNvDecoder(w, h, f, c, g)read_size 4096rt 0fd 0t0 time.time()print(running stream)# nv_cvt nvc.PySurfaceConverter(# w, h, self.nvYuv.Format(), nvc.PixelFormat.RGB, 0# )nv_cvt nvc.PySurfaceConverter(w, h, nvc.PixelFormat.NV12, nvc.PixelFormat.BGR, g)cc_ctx nvc.ColorspaceConversionContext(params[color_space], params[color_range])nv_down nvc.PySurfaceDownloader(w, h, nv_cvt.Format(), g)data np.zeros((w * h, 3), np.uint8)empty_count 0while True:t1time.time()if not read_size:read_size int(rt / fd)rt read_sizefd 1bits proc.stdout.read(read_size)if not len(bits):print(Cant read data from pipe)breakelse:rt len(bits)enc_packet np.frombuffer(bufferbits, dtypenp.uint8)pkt_data nvc.PacketData()try:surf nvdec.DecodeSurfaceFromPacket(enc_packet, pkt_data)if not surf.Empty():fd 1if pkt_data.bsl read_size:read_size pkt_data.bslcvtSurface nv_cvt.Execute(surf, cc_ctx)success nv_down.DownloadSingleSurface(cvtSurface, data)if success:new_data data.reshape((h, w, 3))cv2.imshow(str(thread_index), new_data)cv2.waitKey(1)else:empty_count 1if empty_count framerate * 30:print(surf is Empty too many times str(framerate * 30))nvdec nvc.PyNvDecoder(w, h, f, c, g)empty_count 0except nvc.HwResetException:nvdec nvc.PyNvDecoder(w, h, f, c, g)empty_count 0continuet2 time.time()# print((t2-t1)*1000)if __name__ __main__:add_cuda_dll_directories()print(This sample decodes multiple videos in parallel on given GPU.)print(It doesnt do anything beside decoding, output isnt saved.)print(Usage: SampleDecodeRTSP.py $gpu_id $url1 ... $urlN .)if len(sys.argv) 2:print(Provide gpu ID and input URL(s).)exit(1)gpuID int(sys.argv[1])urls sys.argv[2:]pool ThreadPoolExecutor(max_workerslen(urls))futures []index 0for url in urls:future pool.submit(decode_rtsp_stream, index, url, gpuID)futures.append(future)index 1pool.shutdown()for future in futures:future.result()运行脚本
python rtsp_decoder.py 0 rtsp://admin:a123456710.10.16.26:554/Streaming/Channels/101?transportmodemulticast
VPF库安装
windows11编译VideoProcessingFramework库_random_2011的博客-CSDN博客
