imagecorruptions函数详解

原创已于 2023-10-10 09:16:25 修改 · 633 阅读

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#计算机视觉 #python

于 2023-10-10 09:14:35 首次发布

文章介绍了GitHub上的imagecorruptions库，用于在Python中对图像进行15种类型的增强，如噪声、模糊、失真等。提供了安装方法、函数用法和示例，演示了如何使用该库进行图像增强并保存结果。

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github:https://github.com/bethgelab/imagecorruptions

1.函数库安装

pip install imagecorruptions

2.函数介绍

def corrupt(image, severity=1, corruption_name=None, corruption_number=-1)
    """This function returns a corrupted version of the given image.
    
    Args:
        image (numpy.ndarray):      image to corrupt; a numpy array in [0, 255], expected datatype is np.uint8
                                    expected shape is either (height x width x channels) or (height x width); 
                                    width and height must be at least 32 pixels;
                                    channels must be 1 or 3;
        severity (int):             strength with which to corrupt the image; an integer in [1, 5]
        corruption_name (str):      specifies which corruption function to call, must be one of
                                        'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
                                        'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
                                        'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression',
                                        'speckle_noise', 'gaussian_blur', 'spatter', 'saturate';
                                    the last four are validation corruptions
        corruption_number (int):    the position of the corruption_name in the above list; an integer in [0, 18]; 
                                        useful for easy looping; 15, 16, 17, 18 are validation corruption numbers
    Returns:
        numpy.ndarray:              the image corrupted by a corruption function at the given severity; same shape as input
    """

severity为图像增强的强度，强度区间为[1,5]
corruption_name为增强的类型：分别为是 ‘gaussian_noise’, ‘shot_noise’, ‘impulse_noise’, ‘defocus_blur’, ‘glass_blur’, ‘motion_blur’, ‘zoom_blur’, ‘snow’, ‘frost’, ‘fog’, ‘brightness’, ‘contrast’, ‘elastic_transform’, ‘pixelate’, ‘jpeg_compression’,‘speckle_noise’, ‘gaussian_blur’, ‘spatter’, ‘saturate’ 共15种类型

3.使用教程

from imagecorruptions import corrupt
import cv2
import numpy as np

image = cv2.imread('2.jpg')   #被处理的图片
corrupted_image = corrupt(image, corruption_name='snow', severity=5)
cv2.imshow('Image', image)
cv2.imshow('corrupted_image', corrupted_image)   #显示增强结果
cv2.imwrite('corrupted_image.jpg',corrupted_image)	#保存增强结果
cv2.waitKey(0)