图像处理的多处理（已编辑）

Question

我有以下程序：

daytime_images = os.listdir("D:/TR/Daytime/")
number_of_day_images = len(daytime_images)
day_value = 27

def find_RGB_day(clouds, red, green, blue):
    img = Image.open(clouds)
    img = img.convert('RGB')
    pixels_single_photo = []
    for x in range(img.size[0]):
        for y in range(img.size[1]):
            h, s, v, = img.getpixel((x, y))
            if h <= red and s <= green and v <= blue:
                pixels_single_photo.append((x,y))
    return pixels_single_photo

number = 0

for _ in range(number_of_day_images):
    world_image = ("D:/TR/Daytime/" + daytime_images[number])
    pixels_found = find_RGB_day(world_image, day_value, day_value, day_value)
    coordinates.append(pixels_found)
    number = number+1

已编辑 我想使用多处理器执行该功能，所以我尝试了：

for number in range(number_of_day_images):
    p = multiprocessing.Process(
        target=find_RGB_day,
        args=("D:/TR/IR_Photos/Daytime/" + daytime_images[number],27, 27, 27))
    p.start()
    p.join()
    number = number+1
    coordinates.append(p)

执行的时候出现了AttributeError，不知道怎么解决：

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 126, in _main
    self = reduction.pickle.load(from_parent)
AttributeError: Can't get attribute 'find_RGB_day' on <module '__main__' (built-in)>

我认为这个错误可能与我将图像引入程序的方式有关，我从一个文件夹中获取所有名称，然后选择每个元素的元素，其中 number= number+1

Answer 1

我正在与wagnifico 并行工作并尝试了 Pool（不错的猜测）。 我没有图像文件，所以我使用namedtuple构造了类似的函数以尽可能接近。

from multiprocessing import Pool, TimeoutError
import os, time
import random
from collections import namedtuple


def find_RGB_day(red, green, blue):
    img = Image((256, 256))  # loading your image goes here eg. 256 x 256
    pixels_single_photo = []
    for x in range(img.size[0]):
        for y in range(img.size[1]):
            h, s, v, = random.randrange(255), random.randrange(255), random.randrange(255)
            if h <= red and s <= green and v <= blue:
                pixels_single_photo.append((x,y))
    time.sleep(5)  # del this line, it imitates loading a big file
    return pixels_single_photo


if __name__ == '__main__':
    number_of_day_images = 5
    day_value = 27
    Image = namedtuple('Image',['size'])

    with Pool(processes=4) as pool:  # You can play with processes number
        multiple_results = [pool.apply_async(find_RGB_day, args=(27, 27, 27)) for i in range(number_of_day_images)]
        try:
            [print(res.get()) for res in multiple_results]
        except TimeoutError:
            print("We lacked patience and got a multiprocessing.TimeoutError")

我使用了有关“使用工人池”的 python 文档，您可以找到 HERE。

因此，池不是创建这个进程循环，而是负责所有计算。

for _ in range(number_of_day_images):
    p = multiprocessing.Process(target=find_RGB_day, args=("D:/TR/IR_Photos/Daytime/" + daytime_images[number], 27, 27, 27))
    p.start()
    p.join()

你可以有这样的东西：

multiple_results = [pool.apply_async(find_RGB_day, args=("D:/TR/IR_Photos/Daytime/" + daytime_images[number], 27, 27, 27)]
    try:
        [print(res.get()) for res in multiple_results]

在我的代码示例中，4 个文件在大约 5 秒内加载并构建了一个 multiple_results 数组（因为我们有 4 个工人），最后一个在 5 秒后触发。

[编辑] 我已经下载了图像并使用此代码来获取所需像素的所有坐标。 (27, 27, 27) 对我来说太低了，所以我使用了不同的比例 (31, 90, 170)。

享受吧。

from multiprocessing import Pool, TimeoutError
import time, os
import random
from PIL import Image


def find_RGB_day(clouds, red, green, blue):
    img = Image.open(clouds)
    img = img.convert()
    pixels_single_photo = []
    for x in range(img.size[0]):
        for y in range(img.size[1]):
            # print(img.getpixel((x, y)))
            h, s, v, = img.getpixel((x, y))
            if h <= red and s <= green and v <= blue:
                pixels_single_photo.append((x,y))
    return pixels_single_photo


def create_pool():
    coordinates = []
    with Pool(processes=4) as pool:
        files_to_precess = [pool.apply_async(find_RGB_day,
                                             args=("D/TR/Daytime/" + daytime_images[number], 31, 90, 170))
                            for number in range(number_of_day_images)]
        try:
            coordinates = [res.get() for res in files_to_precess]  # processes get your data in here
        except TimeoutError:
            print("We lacked patience and got a multiprocessing.TimeoutError")
    return coordinates

if __name__ == '__main__':
    daytime_images = os.listdir("D/TR/Daytime/")
    number_of_day_images = len(daytime_images)
    print(number_of_day_images)
    day_value = 27
    coordinates = create_pool()
    [print(res) for res in coordinates]

Answer 2

您应该按照错误消息的说明进行操作并添加一个主模块：

def fun(inputs):
    # your function
    return outputs

if __name__ == '__main__':
   # your main code
   p = multiprocessing.Process(target=fun, args=(inputs,))
   p.start()
   p.join()

更多详情请访问answers for this question。

另外，您的代码中存在错误。调用进程时必须拆分函数和参数：target=fun, args=(inputs,)，就像我在上面的示例中所做的那样。当您发送带有参数的函数时 - target=fun(inputs) - 您实际上并没有调用任何进程，因为您只是将函数 fun 的输出作为目标发送，而不是函数本身。它将引发错误，因为您的函数的输出不可调用（本身不是函数）。

为了适应您的调用，使用多个参数，您可以使用：

for number in range(number_of_days_images):
    p = multiprocessing.Process(
        target=find_RGB_day,
        args=("D:/TR/IR_Photos/Daytime/" + daytime_images[number],
              27, 27, 27)
    )
    # rest of your code ...

此外，我建议您使用pool.Pool.map，它将在所需数量的工作人员之间拆分参数列表并阻止结果。 here 提供了有关如何为具有多个参数的函数实现它的很好的描述。