图片相似度对比

图片相似度对比，就是两个图片进行对比，比对相似度，返回的数据是浮点数；

from PIL import Image


# This module can classfiy the image by Average Hash Method with spilt the image to 16 pieces.
# Then calculate every piece ,consider all data and return the result
#
# author MashiMaroLjc
# version 2016-2-17

#该模块可以通过平均哈希法对图像进行分类，将图像拆分为16个部分。
#然后计算每一块，考虑所有数据并返回结果
#作者MashiMaroLjc
#版本2016-2-17
def calculate(image1, image2):
    g = image1.histogram()
    s = image2.histogram()
    assert len(g) == len(s), "error"

    data = []

    for index in range(0, len(g)):
        if g[index] != s[index]:
            data.append(1 - abs(g[index] - s[index]) / max(g[index], s[index]))
        else:
            data.append(1)

    return sum(data) / len(g)


def split_imgae(image, part_size):
    pw, ph = part_size
    w, h = image.size

    sub_image_list = []

    assert w % pw == h % ph == 0, "error"

    for i in range(0, w, pw):
        for j in range(0, h, ph):
            sub_image = image.crop((i, j, i + pw, j + ph)).copy()
            sub_image_list.append(sub_image)

    return sub_image_list


def classfiy_histogram_with_split(image1, image2, size=(256, 256), part_size=(128, 128)):
    ''' 'image1' and 'image2' is a Image Object.
    You can build it by 'Image.open(path)'.
    'Size' is parameter what the image will resize to it.It's 256 * 256 when it default.
    'part_size' is size of piece what the image will be divided.It's 64*64 when it default.
    This function return the similarity rate betweene 'image1' and 'image2'
    “image1”和“image2”是一个图像对象。
    您可以通过“Image.open（path）”来构建它。
    “大小”是图像调整大小的参数。默认值为256*256。
    “part_size”是分割图像的大小。默认情况下为64*64。
    此函数返回“image1”和“image2”之间的相似度
    '''
    image1 = image1.resize(size).convert("RGB")
    sub_image1 = split_imgae(image1, part_size)

    image2 = image2.resize(size).convert("RGB")
    sub_image2 = split_imgae(image2, part_size)

    sub_data = 0;
    for im1, im2 in zip(sub_image1, sub_image2):
        sub_data += calculate(im1, im2)

    x = size[0] / part_size[0]
    y = size[1] / part_size[1]

    pre = round((sub_data / (x * y)), 3)
    return pre