python_文件操作_复制图片（python复制粘贴图片）

python_文件操作_复制图片（python复制粘贴图片）

import os, shutil# 我们将重点讨论猫狗图像分类，数据集中包含 4000 张猫和狗的图像# （2000 张猫的图像， 2000 张狗的图像）。我们将 2000 张图像用于训练， 1000 张用于验证， 1000# 张用于测试。# The path to the directory where the original# dataset was uncompressed# 原始数据集解压目录的路径original_dataset_dir = '/Users/fchollet/Downloads/kaggle_original_data'# The directory where we will# store our smaller dataset# 保存较小数据集的目录base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'os.mkdir(base_dir)# 分别对应划分后的训练、# 验证和测试的目录# Directories for our training,# validation and test splitstrain_dir = os.path.join(base_dir, 'train')os.mkdir(train_dir)validation_dir = os.path.join(base_dir, 'validation')os.mkdir(validation_dir)test_dir = os.path.join(base_dir, 'test')os.mkdir(test_dir)# 猫的训练图像目录# Directory with our training cat picturestrain_cats_dir = os.path.join(train_dir, 'cats')os.mkdir(train_cats_dir)# 狗的训练图像目录# Directory with our training dog picturestrain_dogs_dir = os.path.join(train_dir, 'dogs')os.mkdir(train_dogs_dir)# 猫的验证图像目录# Directory with our validation cat picturesvalidation_cats_dir = os.path.join(validation_dir, 'cats')os.mkdir(validation_cats_dir)# 狗的验证图像目录# Directory with our validation dog picturesvalidation_dogs_dir = os.path.join(validation_dir, 'dogs')os.mkdir(validation_dogs_dir)# 猫的测试图像目录# Directory with our validation cat picturestest_cats_dir = os.path.join(test_dir, 'cats')os.mkdir(test_cats_dir)# 狗的测试图像目录# Directory with our validation dog picturestest_dogs_dir = os.path.join(test_dir, 'dogs')os.mkdir(test_dogs_dir)# 将前 1000 张猫的图像复制# 到 train_cats_dir# Copy first 1000 cat images to train_cats_dirfnames = ['cat.{}.jpg'.format(i) for i in range(1000)]for fname in fnames: src = os.path.join(original_dataset_dir, fname) dst = os.path.join(train_cats_dir, fname) shutil.copyfile(src, dst) # 将接下来 500 张猫的图像复# 制到 validation_cats_dir# Copy next 500 cat images to validation_cats_dirfnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]for fname in fnames: src = os.path.join(original_dataset_dir, fname) dst = os.path.join(validation_cats_dir, fname) shutil.copyfile(src, dst) # 将接下来的 500 张猫的图像# 复制到 test_cats_dir# Copy next 500 cat images to test_cats_dirfnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]for fname in fnames: src = os.path.join(original_dataset_dir, fname) dst = os.path.join(test_cats_dir, fname) shutil.copyfile(src, dst) # Copy first 1000 dog images to train_dogs_dirfnames = ['dog.{}.jpg'.format(i) for i in range(1000)]for fname in fnames: src = os.path.join(original_dataset_dir, fname) dst = os.path.join(train_dogs_dir, fname) shutil.copyfile(src, dst) # Copy next 500 dog images to validation_dogs_dirfnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]for fname in fnames: src = os.path.join(original_dataset_dir, fname) dst = os.path.join(validation_dogs_dir, fname) shutil.copyfile(src, dst) # Copy next 500 dog images to test_dogs_dirfnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]for fname in fnames: src = os.path.join(original_dataset_dir, fname) dst = os.path.join(test_dogs_dir, fname) shutil.copyfile(src, dst)As a sanity check, let's count how many pictures we have in each training split (train/validation/test):# 我们来检查一下，看看每个分组（训练 / 验证 / 测试）中分别包含多少张图像print('total training cat images:', len(os.listdir(train_cats_dir)))total training cat images: 1000print('total training dog images:', len(os.listdir(train_dogs_dir)))total training dog images: 1000print('total validation cat images:', len(os.listdir(validation_cats_dir)))total validation cat images: 500print('total validation dog images:', len(os.listdir(validation_dogs_dir)))total validation dog images: 500print('total test cat images:', len(os.listdir(test_cats_dir)))total test cat images: 500print('total test dog images:', len(os.listdir(test_dogs_dir)))total test dog images: 500

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