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关于windows:Windows-环境下-Python-环境安装

到 python 官网下载:

https://www.python.org/downlo…

我抉择 Windows embeddable package(64 bit):

下载到本地后,是一个 zip 包:python-3.10.8-embed-amd64,我把它解压后放到这个文件夹:C:\app\python-3.10.8-embed-amd64

而后增加环境变量 path:

之后关上一个命令行窗口,输出 python 回车,看到输入版本号,阐明环境变量失效了:

新建一个 1.py 文件,内容如下:

print('Hello World!')

执行命令行 python 1.py, 能看到 Hello World 的输入:

一些常见谬误

ModuleNotFoundError: No module named ‘PIL’

首先咱们要装置 Python 的包管理工具 pip.

官网:https://pypi.org/project/pip/

我发现官网对 embeddable 包的解释是,它是 Python 的最小包,适宜嵌入到更大的应用程序中。

还是下载一个全家桶吧,毕竟这是官网举荐:

https://www.python.org/downlo…

抉择自定义装置形式:

而后执行 pip instal pillow 装置对应的开发包,扩大名为 whl:

胜利装置。

而后执行上面这段 python 代码:

import base64
import re
from io import BytesIO
from PIL import Image

# 随机字符串
char = list('M3NB6Q#OC?7>!:–;.')

# 色彩值映射字符串
def get_char(r, g, b, alpha=256):
    if alpha == 0:
        return ' '

    grey = (2126 * r + 7152 * g + 722 * b) / 10000

    char_idx = int((grey / (alpha + 1.0)) * len(char))
    return char[char_idx]

# 图片 base64 字符串
img_base64 = '''data:image/png;base64,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'''

# 正则删除头部信息,即 Data URI scheme
image_data = re.sub('^data:image/.+;base64,', '', img_base64)
# 关上图片
img = Image.open(BytesIO(base64.b64decode(image_data)))

img_widht = img.size[0]
img_height = img.size[1]

# 设定缩放比例
scale_width = 0.3  # 0.75
scale_height = 0.1  # 0.5

# 缩放图片
img = img.resize((int(img_widht*scale_width),
                 int(img_height*scale_height)), Image.NEAREST)

# 输入的字符画
text = ''
for i in range(int(img_height*scale_height)):
    for j in range(int(img_widht*scale_width)):
        text += get_char(*img.getpixel((j, i)))
    text += '\n'

print(text)

胜利输入如下后果:

本例代码里我应用的是硬编码的图片的 base64 编码值。

试下这段代码:

from PIL import Image
 
file_path = 'C:\temp\cy.png'
 
img = Image.open(file_path)
imgSize = img.size  #大小 / 尺寸
w = img.width       #图片的宽
h = img.height      #图片的高
f = img.format      #图像格式
 
print(imgSize)
print(w, h, f)

执行代码,遇到如下谬误:

Traceback (most recent call last):
File “c:\temp\2.py”, line 5, in <module>

img = Image.open(file_path)

File “C:\app\python3108\lib\site-packages\PIL\Image.py”, line 3092, in open

fp = builtins.open(filename, "rb")

OSError: [Errno 22] Invalid argument: ‘C:\temp\cy.png’

把绝对路径里的 \ 替换成 / 即可失常工作:

这里打印出的是图片的宽度和高度,以及图片的格局:

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