python怎么实现直播_Python实现直播推流效果

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我们网站的支持！

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时间： 2019-11-26