如果现在回复太晚，我很抱歉，但因为我一直在做类似的事情，我想我可以在某种程度上做出贡献。

也许在OpenCV中你可以使用模板匹配。假设你用的是摄像头

简化图像(可能是阈值?) 应用模板匹配和检查max_val与minMaxLoc

提示:max_val(或min_val取决于所使用的方法)将为您提供数字，较大的数字。为了获得百分比上的差异，使用与相同图像匹配的模板—结果将是100%。

举例的伪代码:

previous_screenshot = ...
current_screenshot = ...

# simplify both images somehow

# get the 100% corresponding value
res = matchTemplate(previous_screenshot, previous_screenshot, TM_CCOEFF)
_, hundred_p_val, _, _ = minMaxLoc(res)

# hundred_p_val is now the 100%

res = matchTemplate(previous_screenshot, current_screenshot, TM_CCOEFF)
_, max_val, _, _ = minMaxLoc(res)

difference_percentage = max_val / hundred_p_val

# the tolerance is now up to you

希望能有所帮助。

一种更有原则的方法是使用全局描述符来比较图像，比如GIST或CENTRIST。这里描述的哈希函数也提供了类似的解决方案。

看看Haar小波是如何由isk-daemon实现的。你可以使用它的imgdb c++代码来实时计算图像之间的差异:

disk -daemon是一个开源的数据库服务器，能够将基于内容的(可视的)图像搜索添加到任何与图像相关的网站或软件。 这项技术允许任何与图像相关的网站或软件的用户在小部件上绘制他们想要查找的图像，并让网站回复他们最相似的图像或简单地在每个图像详细页面请求更多相似的照片。

如何计算这两幅图像的曼哈顿距离呢?得到n*n个值。然后你可以做一些事情，比如行平均，把值减少到n个，然后再用一个函数得到一个值。

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

可以尝试的小事:

将两个图像重新采样为小的缩略图(例如64 x 64)，并将缩略图与某个阈值逐像素进行比较。如果原始图像几乎相同，重新采样的缩略图将非常相似，甚至完全相同。这种方法可以处理特别是在低光场景中可能出现的噪音。如果你调成灰度，效果可能会更好。