A performance-enhance for fast results: since many images have black or white border, you'd expect faster termination by sampling a few random i,j-points from im and test them? Or use modulo arithmetic to traverse the image rows. First we sample(-without-replacement) say 100 random i,j-points; in the unlikely event that isn't conclusive, then we scan it linearly.
Using a custom iterator iterpixels(im). I don't have PIL installed so I can't test this, here's the outline:
import Image
def isColor(r,g,b): # use tuple-unpacking to unpack pixel -> r,g,b
return (r != g != b)
class Image_(Image):
def __init__(pathname):
self.im = Image.open(pathname)
self.w, self.h = self.im.size
def iterpixels(nrand=100, randseed=None):
if randseed:
random.seed(randseed) # For deterministic behavior in test
# First, generate a few random pixels from entire image
for randpix in random.choice(im, n_rand)
yield randpix
# Now traverse entire image (yes we will unwantedly revisit the nrand points once)
#for pixel in im.getpixel(...): # you could traverse rows linearly, or modulo (say) (im.height * 2./3) -1
# yield pixel
def is_grey_scale(img_path="lena.jpg"):
im = Image_.(img_path)
return (any(isColor(*pixel)) for pixel in im.iterpixels())
(Also my original remark stands, first you check the JPEG header, offset 6: number of components (1 = grayscale, 3 = RGB). If it's 1=grayscale, you know the answer already without needing to inspect individual pixels.)