《如何制作类mnist的金融数据集》——2．生成28*28灰度图

2．生成28*28灰度图

有了9类共54000张黑白图后，需要对它进行进一步的处理，那就是把它弄成28*28的黑像素图像。主要思路就是对每类图像的文件夹进行遍历，然后resize

直接上代码：

import torchvision.transforms as transforms
from PIL import Image

num=6000
for i in range(num):
    # 读取原始图像
    # original_image = Image.open('./pre_data0/{}_0.jpg'.format(i+1))
    # original_image = Image.open('./pre_data1/{}_1.jpg'.format(i+1))
    # original_image = Image.open('./pre_data2/{}_2.jpg'.format(i+1))
    # original_image = Image.open('./pre_data3/{}_3.jpg'.format(i+1))
    # original_image = Image.open('./pre_data4/{}_4.jpg'.format(i+1))
    # original_image = Image.open('./pre_data5/{}_5.jpg'.format(i+1))
    # original_image = Image.open('./pre_data6/{}_6.jpg'.format(i+1))
    # original_image = Image.open('./pre_data7/{}_7.jpg'.format(i+1))
    original_image = Image.open('./pre_data8/{}_8.jpg'.format(i+1))
    # 定义转换
    transform = transforms.Compose([
        transforms.Grayscale(num_output_channels=1),
        transforms.Resize((28, 28)),
        transforms.ToTensor()
    ])
    # 应用转换
    transformed_image = transform(original_image).squeeze(0)  # squeeze(pre_data0)用于去掉批处理维度
    # print(transformed_image)
    for i1 in range(28):
        for j in range(28):
            if transformed_image[i1][j] < 0.1:
                transformed_image[i1][j] = 0
            else:
                transformed_image[i1][j] = 1
    # 将张量转换为图像
    transformed_image_PIL = transforms.ToPILImage()(transformed_image)
    # 保存图像
    # transformed_image_PIL.save('./aft_train_data0/{}_0.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_train_data1/{}_1.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_train_data2/{}_2.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_train_data3/{}_3.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_train_data4/{}_4.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_train_data5/{}_5.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_train_data6/{}_6.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_train_data7/{}_7.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_train_data8/{}_8.jpg'.format(i+1))

    # transformed_image_PIL.save('./aft_test_data0/{}_0.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_test_data1/{}_1.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_test_data2/{}_2.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_test_data3/{}_3.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_test_data4/{}_4.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_test_data5/{}_5.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_test_data6/{}_6.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_test_data7/{}_7.jpg'.format(i+1))
    # transformed_image_PIL.save('./aft_test_data8/{}_8.jpg'.format(i+1))

    # transformed_image_PIL.save('./testdata_png/{}_8.png'.format(i + 1))

    transformed_image_PIL.save('./traindata_png/{}_8.png'.format(i + 1))