Python实现推流直播

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。
在这里插入图片描述

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.cnblogs.com/cocoajin/p/4353767.html

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://blog.csdn.net/jeffery0207/article/details/82958520

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()

参考文章

1. 在Ubuntu 14 上安装 Nginx-RTMP 流媒体服务器:https://www.cnblogs.com/cocoajin/p/4353767.html
1. python mutilprocessing多进程编程:https://blog.csdn.net/jeffery0207/article/details/82958520
1. ffmpeg 将视频和图片互转化:https://blog.csdn.net/TingiBanDeQu/article/details/53896944
1. 基于python2.7的opencv3.3-ffmpeg-rtmp视频处理并推送流直播:https://blog.csdn.net/u014303844/article/details/80394101
1. 读取多个(海康\大华)网络摄像头的视频流 (使用opencv-python)，解决实时读取延迟问题:https://zhuanlan.zhihu.com/p/38136322
1. python利用ffmpeg进行rtmp推流直播:https://zhuanlan.zhihu.com/p/74260950

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Python实现推流直播

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