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[Python] IMG to Char

时间:2017-01-28 09:22:29      阅读:236      评论:0      收藏:0      [点我收藏+]

标签:efault   har   one   else   his   ubi   imp   nbsp   ogr   

Change image into character

from PIL import Image
import argparse

#输入
#命令行输入参数处理
parser = argparse.ArgumentParser()

parser.add_argument(file) #position argument
parser.add_argument(-o,--output) #optional argument and -o is a short option
parser.add_argument(--width, type = int, default = 80) #optional argument
parser.add_argument(--height, type = int, default = 80) 

#获取参数
args = parser.parse_args()

IMG = args.file
WIDTH = args.width
HEIGHT = args.height
OUTPUT = args.output

ascii_char = list("$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\|()1{}[]?-_+~<>i!lI;:,\"^`‘. ")

#处理
#构建映射
def get_char(r,g,b, alpha = 256):
    if alpha == 0:
        return ‘‘
    length = len(ascii_char)
    gray = int(0.2126 * r + 0.7152 * g + 0.0722 * b)

    unit = (256.0+1)/length
    return ascii_char[int(gray/unit)]

#输出
if __name__ == __main__:
#When the Python interpreter reads a source file, it executes all of the code found in it.
#in main program, __name__ = __main__
    im = Image.open(IMG)
    im = im.resize((WIDTH,HEIGHT), Image.NEAREST)
#Image.resize(size, resample=0)
#Parameters:    
#size – The requested size in pixels, as a 2-tuple: (width, height).
#resample – An optional resampling filter. This can be one of PIL.Image.NEAREST (use nearest neighbour), PIL.Image.BILINEAR (linear interpolation), PIL.Image.BICUBIC (cubic spline interpolation), or PIL.Image.LANCZOS (a high-quality downsampling filter). If omitted, or if the image has mode “1” or “P”, it is set PIL.Image.NEAREST.

    txt = ""

    for i in range(HEIGHT):
        for j in range(WIDTH):
            txt += get_char(*im.getpixel((j,i)))
        txt += \n

    print txt

    if OUTPUT:
        with open(OUTPUT, w) as f:
            f.write(txt)
    else:
        with open("output.txt",w) as f:
            f.write(txt)

 

[Python] IMG to Char

标签:efault   har   one   else   his   ubi   imp   nbsp   ogr   

原文地址:http://www.cnblogs.com/KennyRom/p/6354127.html

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