Python实现直播推流效果

更新时间：2020-08-10 02:54:01 作者：startmvc

首先给出展示结果，大体就是检测工业板子是否出现。采取检测的方法比较简单，用的OpenCV

首先给出展示结果，大体就是检测工业板子是否出现。采取检测的方法比较简单，用的OpenCV的模板检测。

大体思路

opencv读取视频
将视频分割为帧
对每一帧进行处理(opencv模板匹配)
在将此帧写入pipe管道
利用ffmpeg进行推流直播

中间遇到的问题

在处理本地视频时，并没有延时卡顿的情况。但对实时视频流的时候，出现了卡顿延时的效果。在一顿度娘操作之后，采取了多线程的方法。

opencv读取视频


def run_opencv_camera():
 video_stream_path = 0 
 # 当video_stream_path = 0 会开启计算机 默认摄像头 也可以为本地视频文件的路径
 cap = cv2.VideoCapture(video_stream_path)

 while cap.isOpened():
 is_opened, frame = cap.read()
 cv2.imshow('frame', frame)
 cv2.waitKey(1)
 cap.release()

OpenCV模板匹配

模板匹配就是在一幅图像中寻找一个特定目标的方法之一，这种方法的原理非常简单，遍历图像中每一个可能的位置，比较各处与模板是否相似，当相似度足够高时，就认为找到了目标。


def template_match(img_rgb):
 # 灰度转换
 img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
 # 模板匹配
 res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
 # 设置阈值
 threshold = 0.8
 loc = np.where(res >= threshold)
 if len(loc[0]):
 # 这里直接固定区域
 cv2.rectangle(img_rgb, (155, 515), (1810, 820), (0, 0, 255), 3)
 cv2.putText(img_rgb, category, (240, 600), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 cv2.putText(img_rgb, Confidence, (240, 640), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 cv2.putText(img_rgb, Precision, (240, 680), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 cv2.putText(img_rgb, product_yield, (240, 720), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 cv2.putText(img_rgb, result, (240, 780), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 5)
 return img_rgb

FFmpeg推流

在Ubuntu 14 上安装 Nginx-RTMP 流媒体服务器

https://www.jb51.net/article/175121.htm


import subprocess as sp
rtmpUrl = ""
camera_path = ""
cap = cv.VideoCapture(camera_path)
# Get video information
fps = int(cap.get(cv.CAP_PROP_FPS))
width = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
# ffmpeg command
command = ['ffmpeg',
 '-y',
 '-f', 'rawvideo',
 '-vcodec','rawvideo',
 '-pix_fmt', 'bgr24',
 '-s', "{}x{}".format(width, height),
 '-r', str(fps),
 '-i', '-',
 '-c:v', 'libx264',
 '-pix_fmt', 'yuv420p',
 '-preset', 'ultrafast',
 '-f', 'flv', 
 rtmpUrl]
# 管道配置
p = sp.Popen(command, stdin=sp.PIPE)
# read webcamera
while(cap.isOpened()):
 ret, frame = cap.read()
 if not ret:
 print("Opening camera is failed")
 break
 # process frame
 # your code
 # process frame
 # write to pipe
 p.stdin.write(frame.tostring())

说明:rtmp是要接受视频的服务器，服务器按照上面所给连接地址即可。

多线程处理

python mutilprocessing多进程编程 https://www.jb51.net/article/134726.htm


def image_put(q):
 # 采取本地视频验证
 cap = cv2.VideoCapture("./new.mp4")
 # 采取视频流的方式
 # cap = cv2.VideoCapture(0)
 # cap.set(cv2.CAP_PROP_FRAME_WIDTH,1920)
 # cap.set(cv2.CAP_PROP_FRAME_HEIGHT,1080)
 if cap.isOpened():
 print('success')
 else:
 print('faild')
 while True:
 q.put(cap.read()[1])
 q.get() if q.qsize() > 1 else time.sleep(0.01)
def image_get(q):
 while True:
 # start = time.time()
 #flag += 1
 frame = q.get()
 frame = template_match(frame)
 # end = time.time()
 # print("the time is", end-start)
 cv2.imshow("frame", frame)
 cv2.waitKey(0)
 # pipe.stdin.write(frame.tostring())
 #cv2.imwrite(save_path + "%d.jpg"%flag,frame)
# 多线程执行一个摄像头
def run_single_camera():
 # 初始化
 mp.set_start_method(method='spawn') # init
 # 队列
 queue = mp.Queue(maxsize=2)
 processes = [mp.Process(target=image_put, args=(queue, )),
 mp.Process(target=image_get, args=(queue, ))]
 [process.start() for process in processes]
 [process.join() for process in processes]
def run():
 run_single_camera() # quick, with 2 threads
 pass

说明:使用Python3自带的多线程模块mutilprocessing模块，创建一个队列，线程A从通过rstp协议从视频流中读取出每一帧，并放入队列中，线程B从队列中将图片取出，处理后进行显示。线程A如果发现队列里有两张图片，即线程B的读取速度跟不上线程A，那么线程A主动将队列里面的旧图片删掉，换新图片。

全部代码展示


import time
import multiprocessing as mp
import numpy as np
import random
import subprocess as sp
import cv2
import os
# 定义opencv所需的模板
template_path = "./high_img_template.jpg"
# 定义矩形框所要展示的变量
category = "Category: board"
var_confidence = (np.random.randint(86, 98)) / 100
Confidence = "Confidence: " + str(var_confidence)
var_precision = round(random.uniform(98, 99), 2)
Precision = "Precision: " + str(var_precision) + "%"
product_yield = "Product Yield: 100%"
result = "Result: perfect"
# 读取模板并获取模板的高度和宽度
template = cv2.imread(template_path, 0)
h, w = template.shape[:2]
# 定义模板匹配函数
def template_match(img_rgb):
 # 灰度转换
 img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
 # 模板匹配
 res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
 # 设置阈值
 threshold = 0.8
 loc = np.where(res >= threshold)
 if len(loc[0]):
 # 这里直接固定区域
 cv2.rectangle(img_rgb, (155, 515), (1810, 820), (0, 0, 255), 3)
 cv2.putText(img_rgb, category, (240, 600), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 cv2.putText(img_rgb, Confidence, (240, 640), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 cv2.putText(img_rgb, Precision, (240, 680), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 cv2.putText(img_rgb, product_yield, (240, 720), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 cv2.putText(img_rgb, result, (240, 780), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 5)
 return img_rgb
# 视频属性
size = (1920, 1080)
sizeStr = str(size[0]) + 'x' + str(size[1])
# fps = cap.get(cv2.CAP_PROP_FPS) # 30p/self
# fps = int(fps)
fps = 11
hz = int(1000.0 / fps)
print ('size:'+ sizeStr + ' fps:' + str(fps) + ' hz:' + str(hz))
rtmpUrl = 'rtmp://localhost/hls/test'
# 直播管道输出
# ffmpeg推送rtmp 重点 ： 通过管道 共享数据的方式
command = ['ffmpeg',
 '-y',
 '-f', 'rawvideo',
 '-vcodec','rawvideo',
 '-pix_fmt', 'bgr24',
 '-s', sizeStr,
 '-r', str(fps),
 '-i', '-',
 '-c:v', 'libx264',
 '-pix_fmt', 'yuv420p',
 '-preset', 'ultrafast',
 '-f', 'flv',
 rtmpUrl]
#管道特性配置
# pipe = sp.Popen(command, stdout = sp.PIPE, bufsize=10**8)
pipe = sp.Popen(command, stdin=sp.PIPE) #,shell=False
# pipe.stdin.write(frame.tostring())
def image_put(q):
 # 采取本地视频验证
 cap = cv2.VideoCapture("./new.mp4")
 # 采取视频流的方式
 # cap = cv2.VideoCapture(0)
 # cap.set(cv2.CAP_PROP_FRAME_WIDTH,1920)
 # cap.set(cv2.CAP_PROP_FRAME_HEIGHT,1080)
 if cap.isOpened():
 print('success')
 else:
 print('faild')
 while True:
 q.put(cap.read()[1])
 q.get() if q.qsize() > 1 else time.sleep(0.01)
# 采取本地视频的方式保存图片
save_path = "./res_imgs"
if os.path.exists(save_path):
 os.makedir(save_path)
def image_get(q):
 while True:
 # start = time.time()
 #flag += 1
 frame = q.get()
 frame = template_match(frame)
 # end = time.time()
 # print("the time is", end-start)
 cv2.imshow("frame", frame)
 cv2.waitKey(0)
 # pipe.stdin.write(frame.tostring())
 #cv2.imwrite(save_path + "%d.jpg"%flag,frame)
# 多线程执行一个摄像头
def run_single_camera():
 # 初始化
 mp.set_start_method(method='spawn') # init
 # 队列
 queue = mp.Queue(maxsize=2)
 processes = [mp.Process(target=image_put, args=(queue, )),
 mp.Process(target=image_get, args=(queue, ))]
 [process.start() for process in processes]
 [process.join() for process in processes]
def run():
 run_single_camera() # quick, with 2 threads
 pass
if __name__ == '__main__':
 run()

总结

以上所述是小编给大家介绍的Python实现直播推流效果，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对脚本之家网站的支持！如果你觉得本文对你有帮助，欢迎转载，烦请注明出处，谢谢！