Problems with using a rough greyscale algorithm?

Question

So I\'m designing a few programs for editing photos in python using PIL and one of them was converting an image to greyscale (I\'m avoiding the use of

Answer 1

The answers provided are enough, but I want to discuss a bit more on this topic in a different manner.

Since I learnt digital painting for interest, more often I use HSV.

It is much more controllable for using HSV during painting, but keep it short, the main point is the S: Saturation separating the concept of color from the light. And turning S to 0, is already the 'computer' grey scale of image.

from PIL import Image
import colorsys

def togrey(img):
    if isinstance(img,Image.Image):
        r,g,b = img.split()
        R = []
        G = []
        B = [] 
        for rd,gn,bl in zip(r.getdata(),g.getdata(),b.getdata()) :
            h,s,v = colorsys.rgb_to_hsv(rd/255.,gn/255.,bl/255.)
            s = 0
            _r,_g,_b = colorsys.hsv_to_rgb(h,s,v)
            R.append(int(_r*255.))
            G.append(int(_g*255.))
            B.append(int(_b*255.))
        r.putdata(R)
        g.putdata(G)
        b.putdata(B)
        return Image.merge('RGB',(r,g,b))
    else:
        return None

a = Image.open('../a.jpg')
b = togrey(a)
b.save('../b.jpg')

This method truly reserved the 'bright' of original color. However, without considering how human eye process the data.