Question

在我的代码中，我不断地从相机抓取框架以检查是否存在任何人体。只要有人，就裁剪身体并将其上传到服务器上。并继续这样做。 问题：每当我启动一个线程将照片上传到服务器时，程序执行就会停止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。我想开始一个单独的线程来上传并行运行的照片，在不干扰正常流量的情况下完成它的工作并在它之后完成。每次检测到身体时都应该这样做。

# 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

Answer 1

校正：

使用cThread = threading.Thread( target= , args=() )定义一个新线程实例
使用cThread.start()启动它，当然，由于您的流程是连续的，因此您没有加入。

简化代码，所以我可以在我的最后测试它：

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：记住线程没有被破坏，你必须确保upload_internet()不会因为任何原因而卡在内存中，或者你可以控制你拥有的实例数量并设置上限和管理僵尸线程以避免进程崩溃和糟糕的内存管理

Python：上传图片而不会干扰程序的正常流程

1 个答案: