Win7，64位，Python中对图片进行 Base64 编码和解码

Base64，简单地讲，就是用 64 个字符来表示二进制数据的方法。这 64 个字符包含小写字母 a-z、大写字母 A-Z、数字 0-9 以及符号"+"、"/"，其实还有一个 "=" 作为后缀用途，所以实际上有 65 个字符。

Python 内置了一个用于 Base64 编解码的库：base64：

编码使用 base64.b64encode()

解码使用 base64.b64decode()

1 对图片进行 Base64 编码和解码

import base64

def convert_image():

# Picture ==> base64 encode

with open('d:\\FileTest\\Hope_Despair.jpg', 'rb') as fin:

image_data = fin.read()

base64_data = base64.b64encode(image_data)

fout = open('d:\\FileTest\\base64_content.txt', 'w')

fout.write(base64_data.decode())

fout.close()

# base64 encode ==> Picture

with open('d:\\FileTest\\base64_content.txt', 'r') as fin:

base64_data = fin.read()

ori_image_data = base64.b64decode(base64_data)

fout = open('d:\\FileTest\\Hope_Despair_2.jpg', 'wb')

fout.write(ori_image_data)

fout.close()

if __name__ == '__main__':

convert_image()

2 对图片进行 Base64 编码和解码（Pythonic）

import base64

def convert_image():

# Picture ==> base64 encode

with open('d:\\FileTest\\Hope_Despair.jpg', 'rb') as fin, open('d:\\FileTest\\base64_content.txt', 'w') as fout:

fout.write(base64.b64encode(fin.read()).decode())

# base64 encode ==> Picture

with open('d:\\FileTest\\base64_content.txt', 'r') as fin, open('d:\\FileTest\\Hope_Despair_2.jpg', 'wb') as fout:

fout.write(base64.b64decode(fin.read()))

if __name__ == '__main__':

convert_image()

import base64

#base_str1 = "data:image/jpg;base64,/9j/4AAQSkZJRgABAgAAAQABAAD/"

base_str= "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"

img= base64.b64decode(base_str)

file= open("./text.jpg","wb")

file.write(img)

file.close()