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python - 如何停止将opencv帧流式传输到浏览器

转载 作者:行者123 更新时间:2023-12-03 22:47:31 32 4
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我正在尝试将 opencv 帧流式传输到浏览器。经过研究,我发现了 Miguel 的教程:
https://blog.miguelgrinberg.com/post/video-streaming-with-flask/page/10

让我分解一下我想要实现的目标:在主页上,我尝试使用 opencv 实时流式传输 opencv 帧,而在另一个页面上,我需要使用网络摄像头拍照。

问题:使用 Miguel 的流式传输到浏览器的方式,启动了一个无限线程,在这种情况下,当我想在另一个页面上拍照时不会释放相机。切换回主页,我收到此错误:

VIDEOIO ERROR: V4L2: Pixel format of incoming image is unsupported by OpenCV
Unable to stop the stream: Device or resource busy
video stream started
OpenCV(3.4.1) Error: Assertion failed (scn == 3 || scn == 4) in cvtColor, file /home/eli/cv/opencv-3.4.1/modules/imgproc/src/color.cpp, line 11115
Debugging middleware caught exception in streamed response at a point where response headers were already sent.



这是我的代码:
detect_face_video.py

这是我进行人脸识别的地方
# import the necessary packages
from imutils.video import VideoStream
import face_recognition
import argparse
import imutils
import pickle
import time
import cv2
from flask import Flask, render_template, Response
import sys
import numpy
from app.cv_func import draw_box
import redis
import datetime
from app.base_camera import BaseCamera



import os


global red
red = redis.StrictRedis(host='localhost', port=6379, db=0, decode_responses=True)



class detect_face:



def gen(self):
i=1
while i<10:
yield (b'--frame\r\n'
b'Content-Type: text/plain\r\n\r\n'+str(i)+b'\r\n')
i+=1


def get_frame(self):

dir_path = os.path.dirname(os.path.realpath(__file__))
# load the known faces and embeddings
print("[INFO] loading encodings...")
"rb").read())
data = pickle.loads(open("%s/encode.pickle"%dir_path, "rb").read())

# initialize the video stream and pointer to output video file, then
# allow the camera sensor to warm up
print("[INFO] starting video stream...")

try:
vs = VideoStream(src=1).start()

except Exception as ex:
vs.release()



print("video stream started")


# loop over frames from the video file stream
i=1
counter = 1
while True:

# grab the frame from the threaded video stream
try:
frame = vs.read()
except Exception as ex:
print("an error occured here")
print(ex)
# finally:
continue

# convert the input frame from BGR to RGB then resize it to have
# a width of 750px (to speedup processing)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
rgb = imutils.resize(frame, width=450, height=400)
r = frame.shape[1] / float(rgb.shape[1])


# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input frame, then compute
# the facial embeddings for each face
boxes = face_recognition.face_locations(rgb,
model="hog")
# boxes = face_recognition.face_locations(rgb,
# model=args["detection_method"])
encodings = face_recognition.face_encodings(rgb, boxes)
names = []


# loop over the facial embeddings

for encoding in encodings:
print(encoding)
# attempt to match each face in the input image to our known
# encodings
matches = face_recognition.compare_faces(data["encodings"],
encoding)
# matches = face_recognition.compare_faces(data["encodings"],
# encoding)
name = "Unknown"

# check to see if we have found a match
if True in matches:
# find the indexes of all matched faces then initialize a
# dictionary to count the total number of times each face
# was matched
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}

# loop over the matched indexes and maintain a count for
# each recognized face face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1

# determine the recognized face with the largest number
# of votes (note: in the event of an unlikely tie Python
# will select first entry in the dictionary)
name = max(counts, key=counts.get)

# update the list of names
names.append(name)
red.set('currentName', name)



# self.create_report(name, counter)
# f = open("tester.txt", 'w+')
key='StudentName%d'%counter

if(name != 'Unknown'):
red.set(key,name)
red.set('counter', counter)



counter+=1

# loop over the recognized faces
for ((top, right, bottom, left), name) in zip(boxes, names):
# rescale the face coordinates
top = int(top * r)
right = int(right * r)
bottom = int(bottom * r)
left = int(left * r)
# print("top: %d right: %d bottom: %d left: %d"%(top,right,bottom,left))
# print("top_: %d right_: %d bottom_: %d left_: %d"%(top_,right_,bottom_,left_))

# draw the predicted face name on the image
cv2.rectangle(frame, (left, top), (right, bottom),
(0, 255, 0), 2)
# draw_box(frame, int(left/2), int(top/2), int(right/2), int(bottom/2))
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)

imgencode=cv2.imencode('.jpg',frame)[1]
stringData = imgencode.tostring()
yield(b'--frame\r\n'
b'Content-Type: text/plain\r\n\r\n'+stringData+b'\r\n')
i+=1

del(vs)
cv2.destroyAllWindows()
vs.stop()

和路由文件(我只粘贴了重要部分):
路线.py
 from flask import Flask, render_template, request,Response,jsonify,make_response
from app.detect_face_video import detect_face
detect = detect_face()


@app.route('/')
def index():
return render_template('index.html')


def get_frame_():
detect.gen()
detect.get_frame()




@app.route('/calc')
def calc():
#This function displays the video streams in the webpage

# detect.vs.stop()
return Response(detect.get_frame(),mimetype='multipart/x-mixed-replace; boundary=frame')

我如何在离开该页面(主页)时停止或说暂停流?

最佳答案

如果您正在寻找更快、更强大且更简单的方式将帧流式传输到浏览器,那么您可以使用我的 VidGear Python 库的 WebGear ,这是一个强大的 ASGI Video-streamer API,建立在 Starlette 之上- 一个轻量级的 ASGI 异步框架/工具包。
要求:仅适用于 Python 3.6+。

# install VidGear
python3 -m pip install vidgear[asyncio]

然后你可以使用这个完整的 python 示例,它在地址 http://<host-machine ip>:8000/ 上运行视频服务器在网络上的任何浏览器上,只需几行代码:
# import required libraries
import uvicorn
from vidgear.gears.asyncio import WebGear

# various performance tweaks
options = {
"frame_size_reduction": 40,
"jpeg_compression_quality": 80,
"jpeg_compression_fastdct": True,
"jpeg_compression_fastupsample": False,
}

# initialize WebGear app
web = WebGear(source="foo.mp4", logging=True, **options)

# run this app on Uvicorn server at address http://localhost:8000/
uvicorn.run(web(), host="0.0.0.0", port=8000)

# close app safely
web.shutdown()
Documentation
如果仍然得到一些错误,提出 issue here在其 GitHub 存储库中。

关于python - 如何停止将opencv帧流式传输到浏览器,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/56031947/

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