如何实现图像搜索，文搜图，图搜图，CLIP+faiss向量数据库实现图像高效搜索
这是AIGC的时代，各种GPT大模型生成文本，还有多模态图文并茂大模型，
以及stable diffusion和stable video diffusion 图像生成视频生成等新模型，
层出不穷，如何生成一个图文并貌的文章，怎么在合适的段落加入图像，图像用什么方式获取，
图像可以使用搜索的形式获取，也可以使用stable diffusion生成
今天说说怎么使用搜索的形式获取，这种方式更高效，节省算力，更容易落地
clip模型，详细可以查看知乎
https://zhuanlan.zhihu.com/p/511460120
或论文https://arxiv.org/pdf/2103.00020.pdf
什么是faiss数据库
Faiss的全称是Facebook AI Similarity Search，是FaceBook的AI团队针对大规模相似度检索问题开发的一个工具，使用C++编写，有python接口，对10亿量级的索引可以做到毫秒级检索的性能。
简单来说，Faiss的工作，就是把我们自己的候选向量集封装成一个index数据库，它可以加速我们检索相似向量TopK的过程，其中有些索引还支持GPU构建，可谓是强上加强。
https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/
1.huggingface下载clip模型，默认是英文版，也有中文版，英文版的效果会更好些
英文版
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
# url = "http://images.cocodataset.org/val2017/000000039769.jpg"
# image = Image.open(requests.get(url, stream=True).raw)
# inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
# image_features = model.get_image_features(inputs["pixel_values"])
# text_features = model.get_text_features(inputs["input_ids"],inputs["attention_mask"])
# outputs = model(**inputs)
# logits_per_image = outputs.logits_per_image # this is the image-text similarity score
# probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
# print(probs)
中文版
from PIL import Image
import requests
from transformers import ChineseCLIPProcessor, ChineseCLIPModel
import torch
device = torch.device("mps")
model = ChineseCLIPModel.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
processor = ChineseCLIPProcessor.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
# url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg"
# image = Image.open(requests.get(url, stream=True).raw)
# Squirtle, Bulbasaur, Charmander, Pikachu in English
# texts = ["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"]
# # compute image feature
# inputs = processor(images=image, return_tensors="pt")
# image_features = model.get_image_features(**inputs)
# image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) # normalize
# # compute text features
# inputs = processor(text=texts, padding=True, return_tensors="pt")
# text_features = model.get_text_features(**inputs)
# text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True) # normalize
# # compute image-text similarity scores
# inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)
# outputs = model(**inputs)
# logits_per_image = outputs.logits_per_image # this is the image-text similarity score
# probs = logits_per_image.softmax(dim=1) # probs: [[1.2686e-03, 5.4499e-02, 6.7968e-04, 9.4355e-01]]
2.可以爬一些图片，做图像库，搜索也是在这个图像库中搜索，这个爬取的图像内容和业务场景相关，
比如你想获取动物的图像，那主要爬动物的就可以，这是我随便下载的一些图片
3.把图像映射成向量，存储在向量数据库faiss中
# from clip_model import model,processor
import faiss
from PIL import Image
import os
import json
from chinese_clip import model,processor
from tqdm import tqdm
d = 512
index = faiss.IndexFlatL2(d) # 使用 L2 距离
# 文件夹路径
# folder_path = '/Users/smzdm/Downloads/Animals_with_Attributes2 2/JPEGImages'
folder_path = "image"
# 遍历文件夹
file_paths = []
for root, dirs, files in os.walk(folder_path):
for file in files:
# 检查文件是否为图片文件（这里简单地检查文件扩展名）
if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif')):
file_path = os.path.join(root, file)
file_paths.append(file_path)
id2filename = {idx:x for idx,x in enumerate(file_paths)}
# 保存为 JSON 文件
with open('id2filename.json', 'w') as json_file:
json.dump(id2filename, json_file)
for file_path in tqdm(file_paths,total=len(file_paths)):
# 使用PIL打开图片
image = Image.open(file_path)
inputs = processor(images=image, return_tensors="pt", padding=True)
image_features = model.get_image_features(inputs["pixel_values"])
image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) # normalize
image_features = image_features.detach().numpy()
index.add(image_features)
# 关闭图像，释放资源
image.close()
faiss.write_index(index, "image.faiss")
4.加载数据库文件和索引文件，使用文本搜索图像或图像搜索图像
# from clip_model import model,processor
import faiss
from PIL import Image
import os
import json
from chinese_clip import model,processor
d = 512
index = faiss.IndexFlatL2(d) # 使用 L2 距离
# 保存为 JSON 文件
with open('id2filename.json', 'r') as json_file:
id2filename = json.load(json_file)
index = faiss.read_index("image.faiss")
def text_search(text,k=1):
inputs = processor(text=text, images=None, return_tensors="pt", padding=True)
text_features = model.get_text_features(inputs["input_ids"],inputs["attention_mask"])
text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True) # normalize
text_features = text_features.detach().numpy()
D, I = index.search(text_features, k) # 实际的查询
filenames = [[id2filename[str(j)] for j in i] for i in I]
return text,D,filenames
def image_search(img_path,k=1):
image = Image.open(img_path)
inputs = processor(images=image, return_tensors="pt")
image_features = model.get_image_features(**inputs)
image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) # normalize
image_features = image_features.detach().numpy()
D, I = index.search(image_features, k) # 实际的查询
filenames = [[id2filename[str(j)] for j in i] for i in I]
return img_path,D,filenames
if __name__ == "__main__":
text = ["雪山","熊猫","长城","苹果"]
text,D,filenames = text_search(text)
print(text,D,filenames)
# img_path = "image/apple2.jpeg"
# img_path,D,filenames = image_search(img_path,k=2)
# print(img_path,D,filenames)
比如用文字搜索
["雪山","熊猫","长城","苹果"]
返回结果：
['雪山', '熊猫', '长城', '苹果'] [[1.2182312]
[1.1529984]
[1.1177421]
[1.1656866]] [['image/OIP (10).jpeg'], ['image/OIP.jpeg'], ['image/OIP (8).jpeg'], ['image/apple2.jpeg']]
还可以使用图片搜图片，打开下面的注释
返回结果
image/apple2.jpeg [[0. 0.11877532]] [['image/apple2.jpeg', 'image/OIP (14).jpeg']]
第一张图像是本身，完全相似，第二张可以看到是一个苹果
标签：inputs,features,搜图,text,image,搜索,图像,import,model From： https://www.cnblogs.com/LiuXinyu12378/p/17874543.html