不能选择<type ' cv2.BRISK '>：属性查找cv2.BRISK失败

Question

我试图同时运行多个CMT跟踪器。因此，我正在设置一个线程池：

import argparse
import cv2
from multiprocessing import Pool
import numpy as np
import os
import sys
import time

import VARtracker
import util

CMT1 = VARtracker.CMT()

... # code lines removed

# Clean up
cv2.destroyAllWindows()

if args.inputpath is not None:
    # If a path to a file was given, assume it is a single video file
    if os.path.isfile(args.inputpath):
        cap = cv2.VideoCapture(args.inputpath)
        # Skip first frames if required
        if args.skip is not None:
            cap.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, args.skip)

    # Otherwise assume it is a format string for reading images
    else:
        cap = util.FileVideoCapture(args.inputpath)
        # Skip first frames if required
        if args.skip is not None:
            cap.frame = 1 + args.skip

    # Check if videocapture is working
    if not cap.isOpened():
        print 'Unable to open video input.'
        sys.exit(1)

    # Read first frame
    status, im0 = cap.read()
    im_gray0 = cv2.cvtColor(im0, cv2.COLOR_BGR2GRAY)
    im_draw = np.copy(im0)

# Getting initial bounding boxes
tl1 = [405, 160]
br1 = [450, 275]

VARtracker.initialise(CMT1, im_gray0, tl1, br1)

frame = 1
while True:
    pool = Pool(processes=4)
    print frame

    # Read image
    status, im = cap.read()
    if not status:
        break
    im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    im_draw = np.copy(im)

    tic = time.time()
    # Serial approach
    #res1 = VARtracker.process_frame(CMT1, im_gray)

    # Parallel approach
    res1 = pool.apply_async(VARtracker.process_frame, (CMT2, im_gray))
    pool.close()
    pool.join()
    res1 = res1.get()
    toc = time.time()

    # Display results
    if res1.has_result:
        cv2.line(im_draw, res1.tl, res1.tr, (255, 0, 0), 4)
        cv2.line(im_draw, res1.tr, res1.br, (255, 0, 0), 4)
        cv2.line(im_draw, res1.br, res1.bl, (255, 0, 0), 4)
        cv2.line(im_draw, res1.bl, res1.tl, (255, 0, 0), 4)

    if not args.quiet:
        cv2.imshow('main', im_draw)
        cv2.waitKey(pause_time)

    # Remember image
    im_prev = im_gray
    frame += 1

每当我评论Serial 并尝试使用线程(Parallel )时，我会遇到以下错误：

回溯(最近一次调用)： 文件"/home/rafael/GIT/CMT-Tracker/VaretoCMT/VARmain.py"，第128行，在模块res1 = res1.get()中 文件“/usr/lib/python2.7/multiprocess.py”，第558行，在get raise self._value cPickle.PicklingError: line : attribute lookup cv2.BRISK失败

其他文件可以在VARmain.py、VARtracker.py和util.py上遇到。

我尝试了很多方法，但仍然没有找到克服Python限制的方法。我发现我不能序列化类方法，只能序列化函数。如果可能的话，我想使用Python标准库来解决它。

Answer 1

我设法解决了。感谢“马特”和“山内子”。基本上，我将读取图像的块移动到worker函数中。因此，如果池大小=6，并且有6个边框，则每个帧将被读取6次(在每个工作人员中)。这是我找到的唯一能让它发挥作用的方法。

当前版本可以找到这里。

import cv2 as cv
import multiprocessing as mp
import time

def worker(folder_path, list_name, top_left, bot_right, index):
    frame_path = folder_path + '/' + list_name[0]
    image_0 = cv.imread(frame_path)
    gray_0 = cv.cvtColor(image_0, cv.COLOR_BGR2GRAY)

    cmt = VARtracker.CMT()
    cmt.initialise(gray_0, top_left, bot_right)
    box_queue = mp.Queue()

    for name in list_name:
        frame_path = folder_path + '/' + name
        image_now = cv.imread(frame_path)
        gray_now = cv.cvtColor(image_now, cv.COLOR_BGR2GRAY)

        cmt.process_frame(gray_now)
        if cmt.has_result:
            print index, name, zip(cmt.tl, cmt.br)
            output.put((index, name, zip(cmt.tl, cmt.br)))
    print 'Process {} finished'.format(index)

def VARmethod(folder_path, final_frame, top_left, bot_right):
    tic = time.time()

    if len(top_left) == len(bot_right):
        list_frame = [index for index in range(1, final_frame + 1)]
        list_name = [str(index) + '.jpg' for index in list_frame]

        pool = mp.Pool(5)
        for index in range(0, len(top_left)):
            pool.apply_async(worker, args=(folder_path, list_name, top_left[index], bot_right[index], index))
        pool.close()
        pool.join()

        print 'Finished with the script'

    toc = time.time()
    print output.qsize()
    print (toc - tic)

Answer 2

在类的周围尝试这个代码段(这不是我的代码，归功于Steven )--这是泡菜类的一种解决办法；多处理模块使用泡菜将作业发送给工人：

def _pickle_method(method):
    func_name = method.im_func.__name__
    obj = method.im_self
    cls = method.im_class
    return _unpickle_method, (func_name, obj, cls)

def _unpickle_method(func_name, obj, cls):
    for cls in cls.mro():
        try:
            func = cls.__dict__[func_name]
        except KeyError:
            pass
        else:
            break
    return func.__get__(obj, cls)

import copy_reg
import types
copy_reg.pickle(types.MethodType, _pickle_method, _unpickle_method)

与multiprocessing这里使用Can't pickle when using python's multiprocessing Pool.map()的示例

并不是说这会很容易转化。如果您真的想要多线程，我建议使用OpenMP的Cython。您只需重写程序中需要与nogil语句并行的部分和并行循环的from cython.parallel cimport prange .