1. 余弦相似度

from PIL import Image
from numpy import average, dot, linalg

# 对图片进行统一化处理
def get_thum(img_path, size=(64, 64), greyscale=False):
    img = Image.open(img_path)
    # 对图像大小重置, Image.ANTIALIAS为高质量的
    img = img.resize(size, Image.ANTIALIAS)
    if greyscale:
        # 将图片转换为L模式，其为灰度图，其每个像素用8个bit表示
        img = img.convert('L')
    return img

# 计算图片的余弦距离
def image_cosin_similarity(img1_path, img2_path):
    img1 = get_thum(img1_path)
    img2 = get_thum(img2_path)
    imgs = [img1, img2]
    vectors = []
    norms = []
    for img in imgs:
        vector = []
        for pixel_tuple in img.getdata():
            vector.append(average(pixel_tuple))
        vectors.append(vector)
        # linalg=linear（线性）+algebra（代数），norm则表示范数
        norms.append(linalg.norm(vector, 2))
    # dot返回的是点积，对二维数组（矩阵）进行计算