教程

https://codingchaozhang.blog.csdn.net/article/details/102732555?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.channel_param&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.channel_param

Python實現(xiàn)推流直播

首先給出展示結(jié)果，大體就是檢測工業(yè)板子是否出現(xiàn)。采取檢測的方法比較簡單，用的OpenCV的模板檢測。

大體思路

opencv讀取視頻

將視頻分割為幀

對每一幀進(jìn)行處理(opencv模板匹配)

在將此幀寫入pipe管道

利用ffmpeg進(jìn)行推流直播

中間遇到的問題

在處理本地視頻時，并沒有延時卡頓的情況。但對實時視頻流的時候，出現(xiàn)了卡頓延時的效果。在一頓度娘操作之后，采取了多線程的方法。

FFmpeg推流

在Ubuntu 14 上安裝 Nginx-RTMP 流媒體服務(wù)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())

說明:rtmp是要接受視頻的服務(wù)器，服務(wù)器按照上面所給連接地址即可。

多線程處理

python mutilprocessing多進(jìn)程編程 https://blog.csdn.net/jeffery0207/article/details/82958520

def image_put(q):

# 采取本地視頻驗證

cap = cv2.VideoCapture("./new.mp4")

# 采取視頻流的方式

# 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)

# 多線程執(zhí)行一個攝像頭

def run_single_camera():

# 初始化

mp.set_start_method(method='spawn') # init

# 隊列

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

說明:使用Python3自帶的多線程模塊mutilprocessing模塊，創(chuàng)建一個隊列，線程A從通過rstp協(xié)議從視頻流中讀取出每一幀，并放入隊列中，線程B從隊列中將圖片取出，處理后進(jìn)行顯示。線程A如果發(fā)現(xiàn)隊列里有兩張圖片，即線程B的讀取速度跟不上線程A，那么線程A主動將隊列里面的舊圖片刪掉，換新圖片。

import time

import multiprocessing as mp

import numpy as np

import random

import subprocess as sp

import cv2

import os

# 定義opencv所需的模板

template_path = "./high_img_template.jpg"

# 定義矩形框所要展示的變量

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"

# 讀取模板并獲取模板的高度和寬度

template = cv2.imread(template_path, 0)

h, w = template.shape[:2]

# 定義模板匹配函數(shù)

def template_match(img_rgb):

# 灰度轉(zhuǎn)換

img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)

# 模板匹配

res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)

# 設(shè)置閾值

threshold = 0.8

loc = np.where(res >= threshold)

if len(loc[0]):

# 這里直接固定區(qū)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

# 視頻屬性

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'

# 直播管道輸出

# ffmpeg推送rtmp 重點 ： 通過管道 共享數(shù)據(jù)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):

# 采取本地視頻驗證

cap = cv2.VideoCapture("./new.mp4")

# 采取視頻流的方式

# 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)

# 采取本地視頻的方式保存圖片

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)

# 多線程執(zhí)行一個攝像頭

def run_single_camera():

# 初始化

mp.set_start_method(method='spawn') # init

# 隊列

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()

參考文章

在Ubuntu 14 上安裝 Nginx-RTMP 流媒體服務(wù)器:https://www.cnblogs.com/cocoajin/p/4353767.html

python mutilprocessing多進(jìn)程編程:https://blog.csdn.net/jeffery0207/article/details/82958520

ffmpeg 將視頻和圖片互轉(zhuǎn)化:https://blog.csdn.net/TingiBanDeQu/article/details/53896944

基于python2.7的opencv3.3-ffmpeg-rtmp視頻處理并推送流直播:https://blog.csdn.net/u014303844/article/details/80394101

讀取多個(海康\大華)網(wǎng)絡(luò)攝像頭的視頻流 (使用opencv-python)，解決實時讀取延遲問題:https://zhuanlan.zhihu.com/p/38136322

python利用ffmpeg進(jìn)行rtmp推流直播:https://zhuanlan.zhihu.com/p/74260950

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