distinguish.py

from PIL import Image, ImageTk
from numpy import average, dot, linalg
i = 1
List=[]
for i in range(9):
    for j in range(9):
        for k in range(1,37):
            image1 = Image.open(r'D:\software_practice\get\question(29)' + str(i) + '.jpg')
            image2 = Image.open(r'D:\software_practice\无框字符\\test(' + str(k) + ')' + str(j) + '.jpg')

            # 对图片进行统一化处理
            def get_thum(image, size=(64, 64), greyscale=False):
                # 利用image对图像大小重新设置, Image.ANTIALIAS为高质量的
                image = image.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
                    image = image.convert('L')
                return image

            # 计算图片的余弦距离
            def image_similarity_vectors_via_numpy(image1, image2):
                image1 = get_thum(image1)
                image2 = get_thum(image2)
                images = [image1, image2]
                vectors = []
                norms = []
                for image in images:
                    vector = []
                    for pixel_tuple in image.getdata():
                        vector.append(average(pixel_tuple))
                    vectors.append(vector)
                    # linalg=linear（线性）+algebra（代数），norm则表示范数
                    # 求图片的范数
                    norms.append(linalg.norm(vector, 2))
                a, b = vectors
                a_norm, b_norm = norms
                # dot返回的是点积，对二维数组（矩阵）进行计算
                res = dot(a / a_norm, b / b_norm)
                return res


            cosin = image_similarity_vectors_via_numpy(image1, image2)
            if cosin > 0.98:
                print(k, j, i, cosin)
                List.append(j)
print(List)