用AI来对比两个人拍照的是不是同一个东西

用机器学习识别图像边缘，并进行旋转，然后再用余弦距离进行比较。

import cv2
import tensorflow as tf
import numpy as np
from scipy.linalg import norm

# 加载模型
model = tf.saved_model.load('model')

def detect_edges(image):
    # 预处理图片
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    image = cv2.resize(image, (224, 224))
    image = np.expand_dims(image, axis=0)
    image = np.expand_dims(image, axis=-1)

    # 检测边缘
    edges = model.predict(image)

    return edges[0]

def compare_images(image1, image2):
    # 检测边缘
    edges1 = detect_edges(image1)
    edges2 = detect_edges(image2)

    # 计算边缘线段的斜率
    _, _, angle1 = cv2.fitLine(edges1, cv2.DIST_L2, 0, 0.01, 0.01)
    _, _, angle2 = cv2.fitLine(edges2, cv2.DIST_L2, 0, 0.01, 0.01)

    # 计算夹角
    angle1 = np.rad2deg(np.arctan(angle1))
    angle2 = np.rad2deg(np.arctan(angle2))

    # 旋转图片
    rows, cols = image1.shape[:2]
    M1 = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle1, 1)
    rotated_image1 = cv2.warpAffine(image1, M1, (cols, rows))

    rows, cols = image2.shape[:2]
    M2 = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle2, 1)
    rotated_image2 = cv2.warpAffine(image2, M2, (cols, rows))

    # 计算余弦相似度
    similarity = np.dot(rotated_image1.flatten(), rotated_image2.flatten()) / (norm(rotated_image1.flatten()) * norm(rotated_image2.flatten()))
    print(similarity)