1 np.random.permutation

np.random.seed()函数 - 知乎
https://zhuanlan.zhihu.com/p/266472620

顺序要一样，每次得到的结果也一样

import gdal
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from collections import Counter
import time

# np.random.seed后，np.random.permutation出现的顺序必须一致才对应，否则不对应，可以理解为seed是堆
np.random.seed(0)
print(np.random.permutation(10))
print(np.random.permutation(4))
print("第一次")
np.random.seed(0)
print(np.random.permutation(4))
print(np.random.permutation(10))
print(np.random.permutation(4))
print("第二次")
np.random.seed(0)
print(np.random.permutation(10))
print(np.random.permutation(4))
print("第三次")
np.random.seed(0)
print(np.random.permutation(10))
print(np.random.permutation(4))

2 训练集和验证集

def get_voc_datagen(train_img_path,train_label_path,val_img_path=None,val_label_path=None,num_class, batch_size, target_size):
    """
    进行文件的读取和预处理
    :param train_img_path:训练集影像文件夹
    :param train_label_path:
    :param val_img_path:如果验证集为None,就从训练集中拿出20%
    :param val_label_path:
    :param num_class:
    :param batch_size:
    :param img_preprocess:
    :param mask_preprocess:
    :param target_size:
    :return:
    """

    train_imgs = os.listdir(train_img_path)
    train_labels = os.listdir(train_label_path)
    train_image_paths = [os.path.join(train_img_path, imgname) for imgname in train_imgs]
    train_label_paths = [os.path.join(train_label_path, labelname) for labelname in train_labels]
    # 设置随机数种子,之后打乱数据集
    np.random.seed(0)
    index = np.random.permutation(len(train_image_paths))
    train_image_paths = np.array(train_image_paths)[index]
    train_label_paths = np.array(train_label_paths)[index]

    # 如果验证集为None, 就从训练集中拿出20%当作验证集
    if val_img_path == None and val_label_path==None:
        train_number = int(len(train_image_paths) * 0.8)
        val_image_paths = train_image_paths[train_number:]
        val_label_paths = train_label_paths[train_number:]

        train_image_paths = train_image_paths[:train_number]
        train_label_paths = train_label_paths[:train_number]

    else:
        val_images = os.listdir(val_img_path)
        val_labels = os.listdir(val_label_path)
        val_image_paths = [os.path.join(val_img_path, imgname) for imgname in val_images]
        val_label_paths = [os.path.join(val_label_path, imgname) for imgname in val_labels]

    # 检查标签和图像一一对齐
    for i in range(len(train_image_paths)):
        img = train_image_paths[i].split("\\")[-1]
        label = train_label_paths[i].split("\\")[-1]
        if img[:-4] != label[:-4]:
            print("出错了" * 1000)

    for i in range(len(val_image_paths)):
        img = val_image_paths[i].split("\\")[-1]
        label = val_label_paths[i].split("\\")[-1]
        if img[:-4] != label[:-4]:
            print("出错了" * 1000)
	return train_image_paths, train_label_paths,val_image_paths, val_label_paths,