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import torch
from torch import nn
from model import get_model
import glob
import os
import torch.nn.functional as F
from torchvision import transforms
import cv2
import numpy as np
transform = transforms.Compose([
transforms.ToTensor(),
])
def cosine_similarity(v, M):
# 计算向量v的模
v_norm = np.linalg.norm(v)
# 计算矩阵M每一列的模
M_norm = np.linalg.norm(M, axis=0)
# 计算向量v和矩阵M每一列的点积
dot_product = np.dot(v, M)
# 计算余弦相似度
similarity = dot_product / (v_norm * M_norm)
return similarity
if __name__ == '__main__':
weights_path = 'weights/epoch6_loss_8.045684943666645.pth'
img_dir = r'J:\experiment_data\0.1 test\test_img'
target_img_index = 500
img_path = glob.glob(img_dir + os.sep + '*.png')
model = get_model()
model.load_state_dict(torch.load(weights_path))
model.eval()
vectors = []
for i in img_path:
print(i)
img = cv2.imread(i, -1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = transform(img).unsqueeze(0)
vector = model(img)
vector = vector.squeeze().reshape(-1).detach().numpy()
vector = vector.tolist()
vectors.append(vector)
vectors = np.array(vectors, dtype=np.float32).transpose()
print(f'特征矩阵维度是：\n {vectors.shape}')
target_img = cv2.imread(img_path[target_img_index], -1)
target_img = cv2.cvtColor(target_img, cv2.COLOR_BGR2RGB)
target_img = transform(target_img).unsqueeze(0)
target_vector = model(target_img)
target_vector = target_vector.squeeze().reshape(1, -1).detach().numpy()
target_vector = target_vector.astype(np.float32)
cos_similarity = cosine_similarity(target_vector, vectors)
sorted_indices = np.argsort(-cos_similarity)
v_sorted = np.take(cos_similarity, sorted_indices)
# print(f'相似度向量：\n {v_sorted}')
# print(f'相似度序号向量：\n {sorted_indices}')
print(f'排序向量维度：\n {sorted_indices.shape}')
print(f'前10的相似度：\n {v_sorted[0, :10]}')
print(f'前10的图像：\n {sorted_indices[0, :10]}')
print(f'最后10个的相似度：\n {v_sorted[0, -10:]}')
print(f'最后10个的图像：\n {sorted_indices[0, -10:]}')
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