你可以计算这两个图像的直方图，然后计算Bhattacharyya系数，这是一个非常快速的算法，我已经用它来检测板球视频中的镜头变化(在C中使用openCV)

我也遇到了同样的问题，写了一个简单的python模块，使用pillow的ImageChops来比较两个相同大小的图像，创建一个黑白差异图像，并总结直方图值。

你可以直接得到这个分数，也可以得到一个百分比值，与完整的黑白差异进行比较。

它还包含一个简单的is_equal函数，可以在图像传递为相等的情况下(并包括)提供一个模糊阈值。

这种方法不是很详细，但可能对其他正在与相同问题斗争的人有用。

https://pypi.python.org/pypi/imgcompare/

我特别要解决的问题是如何计算它们是否“足够不同”。我假设你能弄清楚如何一个一个地减去像素。

首先，我将取一堆没有任何变化的图像，并找出任何像素变化的最大量，仅仅是因为捕获的变化、成像系统中的噪声、JPEG压缩工件和照明的每时每刻的变化。也许你会发现，即使没有任何移动，1或2位的差异也是可以预期的。

对于“真实”测试，你需要一个这样的标准:

如果最多P个像素的差异不超过E，则相同。

所以，如果E = 0.02, P = 1000，这可能意味着(大约)如果任何单个像素改变超过5个单位(假设8位图像)，或者如果超过1000个像素有任何错误，这将是“不同的”。

这主要是一种很好的“分类”技术，用于快速识别足够接近而不需要进一步检查的图像。“失败”的图像可能更多的是一种更复杂/昂贵的技术，例如，如果相机抖动，或者对光线变化更健壮，就不会产生假阳性。

I run an open source project, OpenImageIO, that contains a utility called "idiff" that compares differences with thresholds like this (even more elaborate, actually). Even if you don't want to use this software, you may want to look at the source to see how we did it. It's used commercially quite a bit and this thresholding technique was developed so that we could have a test suite for rendering and image processing software, with "reference images" that might have small differences from platform-to-platform or as we made minor tweaks to tha algorithms, so we wanted a "match within tolerance" operation.

使用SSIM测量两幅图像之间的结构相似指数度量。

两种流行且相对简单的方法是:(a)已经提出的欧几里得距离，或(b)标准化互相关。与简单的互相关相比，归一化互相关对光照变化的影响明显更强。维基百科给出了一个标准化互相关的公式。更复杂的方法也存在，但它们需要更多的工作。

使用numpy-like语法，

dist_euclidean = sqrt(sum((i1 - i2)^2)) / i1.size

dist_manhattan = sum(abs(i1 - i2)) / i1.size

dist_ncc = sum( (i1 - mean(i1)) * (i2 - mean(i2)) ) / (
  (i1.size - 1) * stdev(i1) * stdev(i2) )

假设i1和i2为二维灰度图像阵列。

import os
from PIL import Image
from PIL import ImageFile
import imagehash
  
#just use to the size diferent picture
def compare_image(img_file1, img_file2):
    if img_file1 == img_file2:
        return True
    fp1 = open(img_file1, 'rb')
    fp2 = open(img_file2, 'rb')

    img1 = Image.open(fp1)
    img2 = Image.open(fp2)

    ImageFile.LOAD_TRUNCATED_IMAGES = True
    b = img1 == img2

    fp1.close()
    fp2.close()

    return b





#through picturu hash to compare
def get_hash_dict(dir):
    hash_dict = {}
    image_quantity = 0
    for _, _, files in os.walk(dir):
        for i, fileName in enumerate(files):
            with open(dir + fileName, 'rb') as fp:
                hash_dict[dir + fileName] = imagehash.average_hash(Image.open(fp))
                image_quantity += 1

    return hash_dict, image_quantity

def compare_image_with_hash(image_file_name_1, image_file_name_2, max_dif=0):
    """
    max_dif: The maximum hash difference is allowed, the smaller and more accurate, the minimum is 0.
    recommend to use
    """
    ImageFile.LOAD_TRUNCATED_IMAGES = True
    hash_1 = None
    hash_2 = None
    with open(image_file_name_1, 'rb') as fp:
        hash_1 = imagehash.average_hash(Image.open(fp))
    with open(image_file_name_2, 'rb') as fp:
        hash_2 = imagehash.average_hash(Image.open(fp))
    dif = hash_1 - hash_2
    if dif < 0:
        dif = -dif
    if dif <= max_dif:
        return True
    else:
        return False


def compare_image_dir_with_hash(dir_1, dir_2, max_dif=0):
    """
    max_dif: The maximum hash difference is allowed, the smaller and more accurate, the minimum is 0.

    """
    ImageFile.LOAD_TRUNCATED_IMAGES = True
    hash_dict_1, image_quantity_1 = get_hash_dict(dir_1)
    hash_dict_2, image_quantity_2 = get_hash_dict(dir_2)

    if image_quantity_1 > image_quantity_2:
        tmp = image_quantity_1
        image_quantity_1 = image_quantity_2
        image_quantity_2 = tmp

        tmp = hash_dict_1
        hash_dict_1 = hash_dict_2
        hash_dict_2 = tmp

    result_dict = {}

    for k in hash_dict_1.keys():
        result_dict[k] = None

    for dif_i in range(0, max_dif + 1):
        have_none = False

        for k_1 in result_dict.keys():
            if result_dict.get(k_1) is None:
                have_none = True

        if not have_none:
            return result_dict

        for k_1, v_1 in hash_dict_1.items():
            for k_2, v_2 in hash_dict_2.items():
                sub = (v_1 - v_2)
                if sub < 0:
                    sub = -sub
                if sub == dif_i and result_dict.get(k_1) is None:
                    result_dict[k_1] = k_2
                    break
    return result_dict


def main():
    print(compare_image('image1\\815.jpg', 'image2\\5.jpg'))
    print(compare_image_with_hash('image1\\815.jpg', 'image2\\5.jpg', 7))
    r = compare_image_dir_with_hash('image1\\', 'image2\\', 10)
    for k in r.keys():
        print(k, r.get(k))


if __name__ == '__main__':
    main()

输出: 假 真正的 image2 jpg image1 5. \ \ 815. jpg image2 jpg image1 6. \ \ 819. jpg image2 jpg image1 7. \ \ 900. jpg image2 jpg image1 8. \ \ 998. jpg image2 jpg image1 9. \ \ 1012. jpg 示例图片: 815. jpg 5. jpg