本文介绍了根据类别将不同的数据增强应用于部分火车集的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

我正在研究机器学习过程以对图像进行分类.我的问题是我的数据集不平衡,在我的5个图像类别中,我一类中有约400张图像,而其他类别中的每一个中都有约20张图像.

I'm working on a machine learning process to classify images. My problem is that my dataset is imbalanced, and in my 5 categories of images, I have about 400 images in of one class, and about 20 images of each of the other classes.

我想通过仅对火车组的某些类别应用数据增强来平衡火车组.

I would like to balance my train set by applying data augmentation only to certain classes of my train set.

这是我用来创建验证集火车的代码:

Here's the code I'm using for creating the train an validation sets:

# Import data
data_dir = pathlib.Path(r"C:\Train set")

# Define train and validation sets (80% - 20%)
batch_size = 32
img_height = 240
img_width = 240

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

这是我应用数据增强的方法,尽管这适用于整个火车:

And here's how I apply data augmentation, although this would be for the entire train set:

# Apply data augmentation
data_augmentation = keras.Sequential(
  [
    layers.experimental.preprocessing.RandomFlip("horizontal",
                                                 input_shape=(img_height,
                                                              img_width,
                                                              3)),
    layers.experimental.preprocessing.RandomRotation(0.1),
    layers.experimental.preprocessing.RandomZoom(0.1),
  ]
)

有什么方法可以进入我的火车,提取那些图像较少的类别,并仅对它们应用数据增强?

Is there any way to go into my train set, extract those categories that have fewer images, and apply data augmentation only to them?

提前谢谢!

推荐答案

我建议不要使用 ImageDataGenerator ,而是使用自定义的 tf.data.Dataset .在映射操作中,您可以对类别进行不同的处理,例如:

I suggest not using ImageDataGenerator but a customized tf.data.Dataset. In a mapping operation, you can treat categories differently, e.g.:

def preprocess(filepath):
    category = tf.strings.split(filepath, os.sep)[0]
    read_file = tf.io.read_file(filepath)
    decode = tf.image.decode_jpeg(read_file, channels=3)
    resize = tf.image.resize(decode, (200, 200))
    image = tf.expand_dims(resize, 0)
    if tf.equal(category, 'tf_astronauts'):
        image = tf.image.flip_up_down(image)
        image = tf.image.flip_left_right(image)
    # image = tf.image.convert_image_dtype(image, tf.float32)
    # category = tf.cast(tf.equal(category, 'tf_astronauts'), tf.int32)
    return image, category

让我演示一下.让我们为您提供一个包含训练图像的文件夹:

Let me demonstrate it. Let's make you a folder with training images:

import tensorflow as tf
import matplotlib.pyplot as plt
import cv2
from skimage import data
from glob2 import glob
import os

cat = data.chelsea()
astronaut = data.astronaut()

for category, picture in zip(['tf_cats', 'tf_astronauts'], [cat, astronaut]):
    os.makedirs(category, exist_ok=True)
    for i in range(5):
        cv2.imwrite(os.path.join(category, category + f'_{i}.jpg'),
                    cv2.cvtColor(picture, cv2.COLOR_RGB2BGR))

files = glob('tf_*\\*.jpg')

现在您拥有以下文件:

['tf_astronauts\\tf_astronauts_0.jpg',
 'tf_astronauts\\tf_astronauts_1.jpg',
 'tf_astronauts\\tf_astronauts_2.jpg',
 'tf_astronauts\\tf_astronauts_3.jpg',
 'tf_astronauts\\tf_astronauts_4.jpg',
 'tf_cats\\tf_cats_0.jpg',
 'tf_cats\\tf_cats_1.jpg',
 'tf_cats\\tf_cats_2.jpg',
 'tf_cats\\tf_cats_3.jpg',
 'tf_cats\\tf_cats_4.jpg']

让我们仅将转换应用于宇航员类别.让我们使用 tf.image 转换.

Let's apply tranformations only to the astronaut category. Let's use the tf.image transformations.

def preprocess(filepath):
    category = tf.strings.split(filepath, os.sep)[0]
    read_file = tf.io.read_file(filepath)
    decode = tf.image.decode_jpeg(read_file, channels=3)
    resize = tf.image.resize(decode, (200, 200))
    image = tf.expand_dims(resize, 0)
    if tf.equal(category, 'tf_astronauts'):
        image = tf.image.flip_up_down(image)
        image = tf.image.flip_left_right(image)
    # image = tf.image.convert_image_dtype(image, tf.float32)
    # category = tf.cast(tf.equal(category, 'tf_astronauts'), tf.int32)
    return image, category

然后,我们制作 tf.data.Dataset :

train = tf.data.Dataset.from_tensor_slices(files).\
    shuffle(10).take(4).map(preprocess).batch(4)

当您迭代数据集时,您会看到只有宇航员被翻转了:

And when you iterate the dataset, you'll see that only the astronaut is flipped:

fig = plt.figure()
plt.subplots_adjust(wspace=.1, hspace=.2)
images, labels = next(iter(train))
for index, (image, label) in enumerate(zip(images, labels)):
    ax = plt.subplot(2, 2, index + 1)
    ax.set_xticks([])
    ax.set_yticks([])
    ax.set_title(label.numpy().decode())
    ax.imshow(image[0].numpy().astype(int))
plt.show()

请注意,为了进行培训,您需要取消注释 preprocess 中的两行,以便它返回一个浮点数数组和一个整数.

Please note, for training you will need to uncomment the two lines in preprocess so it returns an array of floats and an integer.

这篇关于根据类别将不同的数据增强应用于部分火车集的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!

07-22 17:14