File size: 2,982 Bytes
c52fe89 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
#读取数据集，在../dataset/raw_data下按照数据集的完整排序,1.png,2.png,3.png,...保存
import os
import numpy as np
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from tqdm import tqdm
def unpickle(file):
"""读取CIFAR-10数据文件"""
import pickle
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
def save_images_from_cifar10(dataset_path, save_dir):
"""从CIFAR-10数据集中保存图像
Args:
dataset_path: CIFAR-10数据集路径
save_dir: 图像保存路径
"""
# 创建保存目录
os.makedirs(save_dir, exist_ok=True)
# 获取训练集数据
train_data = []
train_labels = []
# 读取训练数据
for i in range(1, 6):
batch_file = os.path.join(dataset_path, f'data_batch_{i}')
if os.path.exists(batch_file):
print(f"读取训练批次 {i}")
batch = unpickle(batch_file)
train_data.append(batch[b'data'])
train_labels.extend(batch[b'labels'])
# 合并所有训练数据
if train_data:
train_data = np.vstack(train_data)
train_data = train_data.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
# 读取测试数据
test_file = os.path.join(dataset_path, 'test_batch')
# if os.path.exists(test_file):
# print("读取测试数据")
# test_batch = unpickle(test_file)
# test_data = test_batch[b'data']
# test_labels = test_batch[b'labels']
# test_data = test_data.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
# else:
test_data = []
test_labels = []
# 合并训练和测试数据
all_data = np.concatenate([train_data, test_data]) if len(test_data) > 0 and len(train_data) > 0 else (train_data if len(train_data) > 0 else test_data)
all_labels = train_labels + test_labels if len(test_labels) > 0 and len(train_labels) > 0 else (train_labels if len(train_labels) > 0 else test_labels)
# 保存图像
print(f"保存 {len(all_data)} 张图像...")
for i, (img, label) in enumerate(tqdm(zip(all_data, all_labels), total=len(all_data))):
img = Image.fromarray(img)
img.save(os.path.join(save_dir, f"{i}.png"))
print(f"完成! {len(all_data)} 张图像已保存到 {save_dir}")
if __name__ == "__main__":
# 设置路径
dataset_path = "../dataset/cifar-10-batches-py"
save_dir = "../dataset/raw_data"
# 检查数据集是否存在，如果不存在则下载
if not os.path.exists(dataset_path):
print("数据集不存在，正在下载...")
os.makedirs("../dataset", exist_ok=True)
transform = transforms.Compose([transforms.ToTensor()])
trainset = torchvision.datasets.CIFAR10(root="../dataset", train=True, download=True, transform=transform)
# 保存图像
save_images_from_cifar10(dataset_path, save_dir) |