关于数据扩充

本文代码将从现有文件夹中选择一些随机图像并进行转换，例如添加噪点、旋转、翻转等等。

Step 1: 图像转换(transformations)

现有的python库例如 OpenCV 或 Pillow 都具备有图像转换的功能。这里我们将在scikit-image 上进行实现。

现在定义一些数据扩建所需要的 transformation functions。

import random

from scipy import ndarray

import skimage as sk

from skimage import transform

from skimage import util

def random_rotation(image_array: ndarray):

# pick a random degree of rotation between 25% on the left and 25% on the right

random_degree = random.uniform(-25, 25)

return sk.transform.rotate(image_array, random_degree)

def random_noise(image_array: ndarray):

# 给图像添加随机噪声

return sk.util.random_noise(image_array)

def horizontal_flip(image_array: ndarray):

# horizontal flip 不需要skimage

return image_array[:, ::-1]

到现在为止我们已经有了三种图像转变方式：随机旋转、随机噪声以及水平翻转。

*Note：*使用scipy.ndarray 来呈现图像。

Step 2: 列出文件夹中所有图片并且read

import random

import os

# my folder path containing some images

folder_path = '/Users/mac/Desktop/DATA/plankton41/train'

# the number of file to generate

num_files_desired = 1000

# loop on all files of the folder and build a list of files paths

images = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]

num_generated_files = 0

while num_generated_files <= num_files_desired:

# random image from the folder

image_path = random.choice(images)

# read image as an two dimensional array of pixels

image_to_transform = sk.io.imread(image_path)

Step 3: Image transformations

# dictionary of the transformations functions we defined earlier

available_transformations = {

'rotate': random_rotation,

'noise': random_noise,

'horizontal_flip': horizontal_flip

}

# random num of transformations to apply

num_transformations_to_apply = random.randint(1, len(available_transformations))

num_transformations = 0

transformed_image = None

while num_transformations <= num_transformations_to_apply:

# choose a random transformation to apply for a single image

key = random.choice(list(available_transformations))

transformed_image = available_transformations[key](image_to_transform)

num_transformations += 1

这里我们将单个图像的变换数量和要应用的变换类型进行随机选取。然后，我们只调用定义在转换字典中的函数(倒数第二行)。

Step 4: 存储新图像

# define a name for our new file

new_file_path = '%s/augmented_image_%s.jpg' % (folder_path, num_generated_files)

# write image to the disk

sk.io.imsave(new_file_path, transformed_image)

Conclusion

现在，你可以通过data augmentation 生成1000个新图像。当然，还可以添加一些额外的变换，对于不同变换发生的可能性也可以自己调整。

希望本文能对你有所帮助～

ps:转自csdn博主爱喝奶茶的Ethan