How to check whether a jpeg image is color or gray

问题:

I have to write a test case in python to check whether a jpg image is in color or grayscale. Can anyone please let me know if there is any way to do it with out installing extra libraries like opencv?

回答1:

Expanding @gat answer:

import Image
def is_grey_scale(img_path="lena.jpg"):
    im = Image.open(img_path).convert('RGB')
    w,h = im.size
    for i in range(w):
        for j in range(h):
            r,g,b = im.getpixel((i,j))
            if r != g != b: return False
    return True

Basically check every pixel to check if it is grayscale (R == G == B)

回答2:

Can be done as follow:

from scipy.misc import imread, imsave, imresize
image = imread(f_name)
if(len(image.shape)<3):
      print 'gray'
elif len(image.shape)==3:
      print 'Color(RGB)'
else:
      print 'others'

回答3:

For faster processing, it is better to avoid loops on every pixel, using ImageChops, (but also to be sure that the image is truly grayscale, we need to compare colors on every pixel and cannot just use the sum):

from PIL import Image,ImageChops

def is_greyscale(im):
    """
    Check if image is monochrome (1 channel or 3 identical channels)
    """
    if im.mode not in ("L", "RGB"):
        raise ValueError("Unsuported image mode")

    if im.mode == "RGB":
        rgb = im.split()
        if ImageChops.difference(rgb[0],rgb[1]).getextrema()[1]!=0: 
            return False
        if ImageChops.difference(rgb[0],rgb[2]).getextrema()[1]!=0: 
            return False
    return True

回答4:

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.)

回答5:

Why wouldn't we use ImageStat module?

from PIL import Image, ImageStat

def is_grayscale(path="image.jpg")

    im = Image.open(path).convert("RGB")
    stat = ImageStat.Stat(im)

    if sum(stat.sum)/3 == stat.sum[0]:
        return True
    else:
        return False

stat.sum gives us a sum of all pixels in list view = [R, G, B] for example [568283302.0, 565746890.0, 559724236.0]. For grayscale image all elements of list are equal.

回答6:

As you are probably correct, OpenCV may be an overkill for this task but it should be okay to use Python Image Library (PIL) for this. The following should work for you:

import Image
im = Image.open("lena.jpg")

EDIT As pointed out by Mark and JRicardo000, you may iterate over each pixel. You could also make use of the im.split() function here.