Python：在不干扰程序正常流程的情况下上传图片

在我的代码中，我不断地从摄像机抓取画面，检查是否有人体存在。只要有人，裁剪尸体并上传到服务器上。继续这样做。 问题：每当我启动一个将照片上传到服务器的线程时，我的程序就会停止执行，并等待上传线程完成。我不希望我的程序执行停止并等待。我希望它不停地运行。我想启动一个单独的线程来上传并行运行的照片，在不干扰正常流程的情况下完成它的工作，并在它完成后完成。每次检测到尸体时，它都应该这样做

# USAGE
# python detect.py --images images
# import the necessary packages
from __future__ import print_function
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
import time
import threading
import Queue
import multiprocessing
import requests
from poster.encode import multipart_encode
from poster.streaminghttp import register_openers
import urllib2
from urllib2 import Request, urlopen, URLError
import Queue
import urllib
import traceback

size = 2
i=0
#Queues to store data
queue_FACES = multiprocessing.Queue()

(im_width, im_height) = (112, 112)

# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

# Capture Camera Stream
#webcam = cv2.VideoCapture('/home/irum/Desktop/WIN_20170529_09_53_13_Pro.mp4')
webcam = cv2.VideoCapture(0)

#h=4.27 w=4.29  AVG = 4.28

# Upload to server
def upload_internet(filename2,sampleFile,check_path1):

    #print("upoading....")
    filename2 = filename2+'.jpg'
    #print (filename2)      

    register_openers()

    datagen, headers = multipart_encode({"sampleFile": open(sampleFile), "name": filename2})
    #request = urllib2.Request("http://videoupload.hopto.org:5000/api/Sync_log", datagen, headers)
    request = urllib2.Request("http://videoupload.hopto.org:5002/api/Synclog", datagen, headers)

    try:
        #print ("***UPLOAD SERVER RESPONSE***")
        response = urllib2.urlopen(request)
        html=response.read() 
        print ("html ",html)

        #resp = json.loads(html) 
        # with open('output_file.txt', "wb") as code: #CHANGE PATH
        #   code.write(curr_time+"\n"+html +"\n")

    except URLError , e:

        if hasattr(e, 'reason'):
            #print ('We failed to reach a server.')
            print ('Reason: ', e.reason)
        elif hasattr(e, 'code'):
            #print ('The server couldn\'t fulfill the request.')
            print ('Error code: ', e.code)

    except Exception:
        print ('generic exception: ' + traceback.format_exc())

while True:
    # read each frame
    ret, frame = webcam.read()
    # resize it
    image = imutils.resize(frame, width=min(300, frame.shape[1]))
    orig = image.copy()

    # detect people in the frame
    (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
        padding=(8, 8), scale=1.05)

    # draw the original bounding boxes
    for i in range(len(rects)):

        body_i = rects[i]
        (x, y, w, h) = [v * 1 for v in body_i]
        cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)

        # apply non-maxima suppression
        rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
        pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)

        # draw the final bounding boxes
        for i in range(len(rects)):

            body_i = rects[i]
            (xA, yA, xB, yB) = [int(v * 1) for v in body_i]

            # rect on scaled image
            cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2) 
            # rects to map on original frame
            (x1, y1, w1, h1) = [int(v * 4.28) for v in body_i]
            cv2.rectangle(frame, (x1, y1), (w1, h1), (0, 45, 255), 2)

            # Crop body from Original frame
            body_big = frame[y1:y1+h1, x1:x1+w1]

            # Save body
            save_body_path = '/home/irum/Desktop/pedestrian-detection/BIG_BODY' 
            cur_date = (time.strftime("%Y-%m-%d"))
            cur_time = (time.strftime("%H:%M:%S"))
            new_pin =cur_date+"-"+cur_time
            filename1 = 'BIG'
            filename2 = str(filename1)+"-"+str(new_pin)  
            print ("filename2",filename2)  
            sampleFile = ('%s/%s.jpg' % (save_body_path, filename2))
            print ("sampleFile",sampleFile)
            cv2.imwrite('%s/%s.jpg' % (save_body_path, filename2), body_big)

            # upload body
            upload_process = threading.Thread(target=upload_internet(filename2,sampleFile,save_body_path))
            upload_process.start()


    # show the output images
    cv2.imshow("Before NMS", orig)
    cv2.imshow("After NMS", image)
    cv2.imshow("BIG BODY", frame)
    # cv2.imshow("FACE", body_big2)
    key = cv2.waitKey(10)
    if key == 27:
        break

更正：

使用cThread=threading.Thread target=，args=定义 新线程实例 使用cThread.start启动它，当然您没有join，因为您的进程是连续的。 简化的代码，以便我可以在我的终端测试运行它：

import time
import threading
import multiprocessing
from time import sleep

def upload_internet(filename,sampleFile,check_path):
    print ("//// WAITING FOR SERVER RESPONSE")
    time.sleep(3)
    print ("RECEIVED SERVER RESPONSE \\\\\\")

filename = "filename"
sampleFile = "sampleFile"
save_body_path = "save_body_path"
key = 1

while True:

    rects = range(0,10)
    # draw the original bounding boxes
    range_len_rects = range(len(rects))

    for i in range_len_rects:

        print("Main starts")

        rects = range(0,10)
        thread_list = []

        for i in range_len_rects:

            # upload body
            thread_list.append ( threading.Thread( target=upload_internet, args=( filename + "-" + str(i) ,sampleFile,save_body_path) ) )
            thread_list[i].start()

            print ("Exiting Launch Thread loop :"+ str(i) + "/" + str(range_len_rects[i]) )

        print("Main sleep for 10 seconds")
        time.sleep(10);
        if key == 27:
            break

---

PS：请记住线程没有被破坏，您必须确保上载internet不会因为任何原因卡在内存中，或者您可以控制您拥有的实例数量，设置cap并管理僵尸线程，以避免进程崩溃和内存管理错误

谢谢！这很有帮助。还有一件事，因为我是python新手。如果我需要再运行两个线程，一个像upload线程，另一个我需要等待线程返回的结果，然后对返回的值执行进一步的操作。我将如何实现这个线程类？首先，如果消息是正确的答案，您应该将其标记为答案，并且在我的回复旁边将有绿色复选框。您可以使用队列的put和get来准备父线程，并等待回复，我个人更喜欢将管道与多处理结合使用，因为我会将其作为一个答案，但我的问题保持不变。但它仍然很有用，因为我是python新手，对线程不太了解。我希望你不介意：p