请提供python代码：用于以并行计算的方式处理5000个图片，同时输入到20个one class svm分类器中

关于您的问题，我可以为您提供一些python代码的示例，用于并行处理5000张图片，并输入到20个one class svm分类器中进行分类。以下是一个简单的示例：

import numpy as np
from multiprocessing import Pool
from sklearn import svm

# Define the function to process a single image
def process_image(image_file):
    # Load the image file
    image_data = load_image(image_file)
    
    # Extract features from the image data
    feature_vector = extract_image_features(image_data)
    
    return feature_vector

# Define the function to process a batch of images
def process_batch(images):
    # Process each image in the batch
    features = [process_image(image) for image in images]
    
    return np.array(features)

# Load the image data
image_files = load_image_files()

# Divide the image data into batches
batch_size = 250
batches = [image_files[i:i+batch_size] for i in range(0, len(image_files), batch_size)]

# Create the one class svm classifiers
classifiers = [svm.OneClassSVM() for i in range(20)]

# Process the image batches in parallel
with Pool(processes=20) as pool:
    batch_features = pool.map(process_batch, batches)

# Feed the feature vectors into the classifiers
for i, classifier in enumerate(classifiers):
    classifier.fit(np.concatenate(batch_features[:, i], axis=0))

这是一个简单示例，请注意，代码中的函数需要您自己去实现，具体实现根据您的数据结构和特征提取方法会有所不同。同时，需要注意的是，在处理大量图片时，可能需要考虑内存使用的问题。这个问题可以通过对图片进行分批次加载来解决。