import random from PIL import Image import numpy as np class DataAugmentation: def __init__(self, dataset): self.dataset = dataset def rotate(self, image, angle): rotated_image = image.rotate(angle) return rotated_image def crop(self, image, crop_size): width, height = image.size left = random.randint(0, width - crop_size) upper = random.randint(0, height - crop_size) right = left + crop_size lower = upper + crop_size cropped_image = image.crop((left, upper, right, lower)) return cropped_image def mirror(self, image): mirrored_image = image.transpose(Image.FLIP_LEFT_RIGHT) return mirrored_image def augment(self, num_samples, crop_size): augmented_dataset = [] for i in range(num_samples): image = Image.open(self.dataset[i]) operations = [self.rotate, self.crop, self.mirror] operation = random.choice(operations) if operation == self.rotate: angle = random.randint(0, 360) augmented_image = self.rotate(image, angle) elif operation == self.crop: augmented_image = self.crop(image, crop_size) else: augmented_image = self.mirror(image) augmented_dataset.append(np.array(augmented_image)) return augmented_dataset
时间: 2024-02-14 14:26:17 浏览: 20
这是一个数据增强的类,用于对数据集进行图像增强操作。它具有以下方法:
- `rotate(image, angle)`:旋转图像,接受一个图像和旋转角度作为参数,并返回旋转后的图像。
- `crop(image, crop_size)`:裁剪图像,接受一个图像和裁剪尺寸作为参数,并返回裁剪后的图像。
- `mirror(image)`:镜像图像,接受一个图像作为参数,并返回镜像后的图像。
- `augment(num_samples, crop_size)`:对数据集进行增强操作,接受增强样本数和裁剪尺寸作为参数,并返回增强后的数据集。
在 `augment` 方法中,随机选择一个操作(旋转、裁剪或镜像),然后根据选择的操作对图像进行相应的处理,并将增强后的图像添加到增强数据集中。
请问还有其他问题吗?
相关问题
""" This code is based on the Torchvision repository, which was licensed under the BSD 3-Clause. """ import os import pickle import sys import numpy as np import torch from PIL import Image from torch.utils.data import Dataset from utils.mypath import MyPath from torchvision.datasets.utils import check_integrity, download_and_extract_archive class simclr_c10(Dataset): base_folder = 'D:/wjd/simclr_c10' filename = "simclr_c10" def __init__(self, root=MyPath.db_root_dir('wjd/simclr_c10/'), train=True, transform=None, download=False): super('wjd/simclr_c10/', self).__init__() self.data = None self.root = root self.transform = transform self.train = train # training set or test set self.classes = ['Al', 'Ag', 'Au', 'Cu', 'W', 'V', 'Ta', 'Mo'] def __getitem__(self, index): """ Args: index (int): Index Returns: dict: {'image': image, 'target': index of target class, 'meta': dict} """ img, target = self.data[index], self.targets[index] img_size = (img.shape[0], img.shape[1]) img = Image.fromarray(img) class_name = self.classes[target] if self.transform is not None: img = self.transform(img) out = {'image': img, 'target': target, 'meta': {'im_size': img_size, 'index': index, 'class_name': class_name}} return out def get_image(self, index): img = self.data[index] return img def __len__(self): return len(self.data) def extra_repr(self): return "Split: {}".format("Train" if self.train is True else "Test") return outside function,这段代码正确吗
这段代码有一些问题:
1. `super('wjd/simclr_c10/', self).__init__()` 这句话不正确,应该是 `super(simclr_c10, self).__init__()`
2. `self.targets` 在代码中没有定义,需要在 `__init__` 函数中将其定义并初始化
3. `return outside function` 这句话不应该出现在类定义中,需要将其删除
4. 另外,`check_integrity` 和 `download_and_extract_archive` 函数在代码中没有使用,可以删除
修正后的代码如下所示:
```
import os
import pickle
import sys
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from utils.mypath import MyPath
class simclr_c10(Dataset):
base_folder = 'D:/wjd/simclr_c10'
filename = "simclr_c10"
def __init__(self, root=MyPath.db_root_dir('wjd/simclr_c10/'), train=True, transform=None, download=False):
super(simclr_c10, self).__init__()
self.data = None
self.targets = []
self.root = root
self.transform = transform
self.train = train # training set or test set
self.classes = ['Al', 'Ag', 'Au', 'Cu', 'W', 'V', 'Ta', 'Mo']
self._load_data()
def _load_data(self):
if self.train:
data_file = os.path.join(self.root, self.base_folder, 'train.pkl')
else:
data_file = os.path.join(self.root, self.base_folder, 'test.pkl')
with open(data_file, 'rb') as f:
data = pickle.load(f)
self.data = data['data']
self.targets = data['targets']
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
dict: {'image': image, 'target': index of target class, 'meta': dict}
"""
img, target = self.data[index], self.targets[index]
img_size = (img.shape[0], img.shape[1])
img = Image.fromarray(img)
class_name = self.classes[target]
if self.transform is not None:
img = self.transform(img)
out = {'image': img, 'target': target, 'meta': {'im_size': img_size, 'index': index, 'class_name': class_name}}
return out
def get_image(self, index):
img = self.data[index]
return img
def __len__(self):
return len(self.data)
def extra_repr(self):
return "Split: {}".format("Train" if self.train is True else "Test")
```
class Animedataset(data.Dataset): def __init__(self): self.img_list = os.listdir(data_path) self.transforms = transforms def __len__(self): return len(self.img_list) def __getitem__(self, index): img = os.path.join(data_path,self.img_list[index]) img = np.array(Image.open(img)) img = self.transforms(image=img)["image"] return img这段代码什么意思
这段代码定义了一个名为 `Animedataset` 的 PyTorch 数据集类,用于加载图像数据集。具体解释如下:
1. `__init__(self)`:构造函数,初始化数据集。在这里,首先获取数据集中所有图像的文件名列表 `self.img_list`。然后定义数据预处理的操作 `self.transforms`,这个操作在之前已经解释过了。
2. `__len__(self)`:返回数据集的大小,即数据集中包含的图像数量。
3. `__getitem__(self, index)`:获取指定索引的数据。在这里,首先根据索引获取对应图像的文件路径,然后使用PIL库读取图像,并将其转换为Numpy数组格式。接着,使用之前定义的数据预处理操作 `self.transforms` 对图像进行预处理,最后返回预处理后的图像数据。
这个类的作用是将数据集中的图像数据加载到内存中,并在每次训练时提供一个 batch 的数据。