根据类别将不同的数据增强应用于部分火车集 [英] Apply different data augmentation to part of the train set based on the category

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.

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