如何预测你和心仪的Ta有没有夫妻相？

基于华为云ModelArts开发的【一键预测你和Ta的CP值】Demo帮你预测CP指数。

该模型利用ssim算法综合计算五官特征相似程度，从而得出CP值。

//夫妻相的原理在当今心理学、生物学仍有很大争议，夫妻相指数高并不意味着两人未来一定会幸福美满，也不能预判彼此关系变好变坏。本案例只适用于AI技术的学习以及情人节娱乐。

1.下载需要的海报文件和字体

import os

import os.path as osp
import moxing as mox
parent = osp.join(os.getcwd(),'Valentine')
if not os.path.exists(parent):
    mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/Valentine',parent)
    if os.path.exists(parent):
        print('Download success')
    else:
        raise Exception('Download Failed')
else:
    print("Model Package already exists!") 

2.使用ssim算法计算夫妻相

import numpy as np
import cv2
import random
import matplotlib.pyplot as plt
from matplotlib import font_manager
import warnings
from scipy.signal import convolve2d 
from PIL import Image,ImageDraw,ImageFont

warnings.filterwarnings('ignore')

def matlab_style_gauss2D(shape=(3,3),sigma=0.5):
    """
    2D gaussian mask - should give the same result as MATLAB's
    fspecial('gaussian',[shape],[sigma])
    """
    m,n = [(ss-1.)/2. for ss in shape]
    y,x = np.ogrid[-m:m+1,-n:n+1]
    h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )
    h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
    sumh = h.sum()
    if sumh != 0:
        h /= sumh
    return h
 
def filter2(x, kernel, mode='same'):
    return convolve2d(x, np.rot90(kernel, 2), mode=mode)
 
def compute_ssim(im1, im2, k1=0.01, k2=0.04, win_size=11, L=255):
    if not im1.shape == im2.shape:
        raise ValueError("Input Imagees must have the same dimensions")
    if len(im1.shape) > 2:
        raise ValueError("Please input the images with 1 channel")
 
    M, N = im1.shape
    C1 = (k1*L)**2
    C2 = (k2*L)**2
    window = matlab_style_gauss2D(shape=(win_size,win_size), sigma=0.5)
    window = window/np.sum(np.sum(window))
 
    if im1.dtype == np.uint8:
        im1 = np.double(im1)
    if im2.dtype == np.uint8:
        im2 = np.double(im2)
 
    mu1 = filter2(im1, window, 'valid')
    mu2 = filter2(im2, window, 'valid')
    mu1_sq = mu1 * mu1
    mu2_sq = mu2 * mu2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = filter2(im1*im1, window, 'valid') - mu1_sq
    sigma2_sq = filter2(im2*im2, window, 'valid') - mu2_sq
    sigmal2 = filter2(im1*im2, window, 'valid') - mu1_mu2
 
    ssim_map = ((2*mu1_mu2+C1) * (2*sigmal2+C2)) / ((mu1_sq+mu2_sq+C1) * (sigma1_sq+sigma2_sq+C2))
 
    return np.mean(np.mean(ssim_map))

def img_show(similarity, img1, img2, name1, name2):
    # similarity = random.uniform(60,100)
    zt = "./Valentine/方正兰亭准黑_GBK.ttf"
    my_font = font_manager.FontProperties(fname = zt,size =20 )

    img1 = cv2.resize(img1, (520, 520))
    img2 = cv2.resize(img2, (520, 520))
    
    imgs = np.hstack([img1, img2])
    imgs2 = imgs[:,:, ::-1]
    
    plt.axis('off')
    plt.title('{0} VS {1} \n CP指数: {2}%'.format(name1, name2, round(similarity, 2)), fontproperties=my_font)
    plt.imshow(imgs2)
    path = "a.jpg"
    cv2.imwrite(path, imgs)

    # img = cv2ImgAddText(imgs, '夫妻相: {}%'.format(round(similarity, 2)), 350, 130, (255, 0 , 0), 50)
    # cv2.imshow('image1 vs image2', img)
    # cv2.waitKey()

3.修改预置的视频和图片

在Valentine文件夹下，有一个预置的1.png和2.png图片，大家可以将里面的图片替换成自己的，图片的名称不建议修改，如果修改成其他的名称，后面的路径也要进行相应的修改。

点击此处上传你和Ta的照片（不会留存照片信息,推理完成后内存数据会自动清除）

上传成功

if __name__ == '__main__':
    name1 = input('请输入图1照片姓名: \n')
    name2 = input('请输入图2照片姓名: \n')
    
    img1_path = 'Valentine/1.png'
    img2_path = 'Valentine/2.png'

    img1 = cv2.imread(img1_path)
    img2 = cv2.imread(img2_path)

    im1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    im2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

    im1 = cv2.resize(im1, (520,520))
    im2 = cv2.resize(im2, (520,520))
    

    similarity = compute_ssim(im1, im2)*100
    if similarity == 100:
        raise ValueError("图片重复! 请重新上传图片")
    random.seed(similarity)
    add_score = random.uniform(1, 100-similarity)
    similarity += add_score
    img_show(similarity, img1, img2, name1, name2)

注意：输入图1图2照片姓名后都需要按下回车键

预测成功：

image = Image.open("a.jpg")
image = image.resize((498,278))

4.打印输出海报

import os
from PIL import Image,ImageDraw,ImageFont,ImageFilter
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

填写创作者名称

右键即可下载海报

海报如下：