Python實現(xiàn)直播推流效果

更新時間：2019年11月26日 11:00:55 作者：mind_programmonkey

這篇文章主要介紹了Python實現(xiàn)直播推流效果，主要是通過opencv讀取視頻對視頻分割為幀，本文通過實例代碼講解的非常詳細，具有一定的參考借鑒價值,需要的朋友可以參考下

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
對每一幀進行處理(opencv模板匹配)
在將此幀寫入pipe管道
利用ffmpeg進行推流直播

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。

opencv讀取視頻

def run_opencv_camera():
 video_stream_path = 0 
 # 當(dāng)video_stream_path = 0 會開啟計算機 默認(rèn)攝像頭 也可以為本地視頻文件的路徑
 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圖像中尋找一個特定目標(biāo)的方法之一，這種方法的原理非常簡單，遍歷圖像中每一個可能的位置，比較各處與模板是否相似，當(dāng)相似度足夠高時，就認(rèn)為找到了目標(biāo)。

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

FFmpeg推流

在Ubuntu 14 上安裝 Nginx-RTMP 流媒體服務(wù)器

http://www.dbjr.com.cn/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())

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多進程編程 http://www.dbjr.com.cn/article/134726.htm

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。

全部代碼展示

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()

總結(jié)

以上所述是小編給大家介紹的Python實現(xiàn)直播推流效果，希望對大家有所幫助，如果大家有任何疑問請給我留言，小編會及時回復(fù)大家的。在此也非常感謝大家對腳本之家網(wǎng)站的支持！
如果你覺得本文對你有幫助，歡迎轉(zhuǎn)載，煩請注明出處，謝謝！

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