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文本的跑通了，接下来玩玩图片场景

1. 引入模型

再另起类test_qdrant_img.py，转化图片用到的模型和文本不太一样，我们这里使用ResNet-50模型

import unittest
from qdrant_client.http.models import Distance, VectorParams
from qdrant_client import QdrantClient
import torch
import torchvision.transforms as transforms
from PIL import Imageclass TestQDrantImg(unittest.TestCase):def setUp(self):self.collection_name = "img_collection"self.client = QdrantClient("localhost", port=6333)# 加载ResNet-50模型self.model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)self.model.eval()# 图像预处理self.preprocess = transforms.Compose([# 图像调整为256*256transforms.Resize(256), # 中心裁剪为224*224transforms.CenterCrop(224), # 转换为张量，像素值从范围[0,255]缩放到范围[0,1]，RGB（红绿蓝）转换为通道顺序（即 RGB 顺序）transforms.ToTensor(), # 应用归一化，减去均值（mean）并除以标准差（std）transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

2. 添加图片向量

我们先创建一个新集合

def test_create_collection(self):self.client.create_collection(collection_name=self.collection_name,vectors_config=VectorParams(size=1000, distance=Distance.EUCLID),)

往集合里分别添加1个猫的图片和1个狗的图片

    def test_img_vector(self):# 加载并预处理图像id = 1image_path = './img/cat1.png'# id = 2# image_path = './img/dog1.png'image = Image.open(image_path)image_tensor = self.preprocess(image)# 在第0维度上添加一个维度，将图像张量转换为形状为 (1, C, H, W) 的张量，其中 C 是通道数，H 是高度，W 是宽度image_tensor = torch.unsqueeze(image_tensor, 0)with torch.no_grad():# 去除维度为1的维度，将特征向量的形状从 (1, D) 转换为 (D,)feature_vector = self.model(image_tensor).squeeze().tolist()operation_info = self.client.upsert(collection_name=self.collection_name,points=[{'id': id, 'vector': feature_vector, 'payload': {"image_path": image_path}}])print(operation_info)

3. 匹配图片向量

然后用其他猫狗的图片来做搜索匹配

    def test_search(self):# 加载并预处理图像image_path = './img/cat2.png'# image_path = './img/dog2.png'# image_path = './img/cat3.png'image = Image.open(image_path)image_tensor = self.preprocess(image)image_tensor = torch.unsqueeze(image_tensor, 0)with torch.no_grad():feature_vector = self.model(image_tensor).squeeze().tolist()search_result = self.client.search(collection_name=self.collection_name, query_vector=feature_vector, limit=3, with_vectors=True, with_payload=True)print(search_result)

结果：

[ScoredPoint(id = 1, version = 0, score = 68.21013, payload = {

'image_path': './img/cat1.png'

}, vector = [...]),

ScoredPoint(id = 2, version = 1, score = 85.10757, payload = {

'image_path': './img/dog1.png'

}, vector = [...])]

当使用猫2猫3作为查询条件时，跟猫1记录的score(向量距离)较小；

同理，使用狗2作为查询条件时，跟狗1记录的score(向量距离)较小