【python】图片转成base64

1. 图片转成base64格式

png_path = ''
with open(png_path, 'rb') as f:
	base64_data = "data:image/png;base64,{}".format(str(base64.b64encode(f.read()).decode('utf8')))

2. base64数据转成 图片

import base64
from PIL import Image
from io import BytesIO
base_str = "data:image/png;base64,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"

head, context = base_str.split(',')
img_data = base64.b64decode(context)
# 直接使用系统工具打开图片, 也可以选择保存图片文件
image = Image.open(BytesIO(img_data))
image.show()
with open('xxx.png', 'wb') as f:
	f.write(img_data)