机器视觉--图像相似度比较之 CLIP or DINOv2--其乐融融的IT技术小站

在人工智能领域，计算机视觉的两大巨头是CLIP和DINOv2。CLIP改变了图像理解的方式，而DINOv2为自监督学习带来了新的方法。在本文中，我们将探索定义CLIP和DINOv2的强项和微妙之处的旅程。我们旨在发现这些模型中哪一个在图像相似度任务的世界中真正出色。让我们见证这两位巨头的较量，看看哪个模型胜出。

CLIP中的图像相似度

使用CLIP计算两个图像之间的相似度是一个简单的过程，只需要两个步骤：首先提取两个图像的特征，然后计算它们的余弦相似度。

首先，确保已安装所需的软件包。建议设置和使用虚拟环境：

#Start by setting up a virtual environment
virtualenv venv-similarity
source venv-similarity/bin/activate
#Install required packages
pip install transformers Pillow torch

接下来，继续计算图像相似度：

import torch
from PIL import Image
from transformers import AutoProcessor, CLIPModel
import torch.nn as nn

device = torch.device('cuda' if torch.cuda.is可用 else "cpu")
processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)

#Extract features from image1
image1 = Image.open('img1.jpg')
with torch.no_grad():
    inputs1 = processor(images=image1, return_tensors="pt").to(device)
    image_features1 = model.get_image_features(**inputs1)

#Extract features from image2
image2 = Image.open('img2.jpg')
with torch.no_grad():
    inputs2 = processor(images=image2, return_tensors="pt").to(device)
    image_features2 = model.get_image_features(**inputs2)

#Compute their cosine similarity and convert it into a score between 0 and 1
cos = nn.CosineSimilarity(dim=0)
sim = cos(image_features1[0],image_features2[0]).item()
sim = (sim+1)/2
print('Similarity:', sim)

2张相似的图像

使用两张相似图像的示例，获得的相似度得分为令人印象深刻的96.4%。

DINOv2中的图像相似度

使用DINOv2计算两张图像之间的相似度的过程与CLIP类似。需要同一组软件包，无需进行任何额外的安装：

from transformers import AutoImageProcessor, AutoModel
from PIL import Image
import torch.nn as nn

device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
model = AutoModel.from_pretrained('facebook/dinov2-base').to(device)


image1 = Image.open('img1.jpg')
with torch.no_grad():
    inputs1 = processor(images=image1, return_tensors="pt").to(device)
    outputs1 = model(**inputs1)
    image_features1 = outputs1.last_hidden_state
    image_features1 = image_features1.mean(dim=1)

image2 = Image.open('img2.jpg')
with torch.no_grad():
    inputs2 = processor(images=image2, return_tensors="pt").to(device)
    outputs2 = model(**inputs2)
    image_features2 = outputs2.last_hidden_state
    image_features2 = image_features2.mean(dim=1)

cos = nn.CosineSimilarity(dim=0)
sim = cos(image_features1[0],image_features2[0]).item()
sim = (sim+1)/2
print('Similarity:', sim)

与CLIP示例中的相同图像对一起使用，使用DINOv2获得的相似度得分为93%。

使用COCO数据集进行测试

在深入评估它们的性能之前，让我们使用COCO数据集的验证集中的图像来比较CLIP和DINOv2的结果。我们采用的流程如下：

遍历数据集以提取所有图像的特征。
将嵌入存储在FAISS索引中。
提取输入图像的特征。
检索相似度最高的三张图像。

对于那些对FAISS深入了解的人，请参考这篇充满信息的文章。确保首先使用以下命令安装它：pip install faiss-[gpu|cpu]。

第1部分：特征提取和创建2个索引：

import torch
from PIL import Image
from transformers import AutoProcessor, CLIPModel, AutoImageProcessor, AutoModel
import faiss
import os
import numpy as np

device = torch.device('cuda' if torch.cuda.is_available() else "cpu")

#Load CLIP model and processor
processor_clip = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
model_clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)

#Load DINOv2 model and processor
processor_dino = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
model_dino = AutoModel.from_pretrained('facebook/dinov2-base').to(device)

#Retrieve all filenames
images = []
for root, dirs, files in os.walk('./val2017/'):
    for file in files:
        if file.endswith('jpg'):
            images.append(root  + '/'+ file)


#Define a function that normalizes embeddings and add them to the index
def add_vector_to_index(embedding, index):
    #convert embedding to numpy
    vector = embedding.detach().cpu().numpy()
    #Convert to float32 numpy
    vector = np.float32(vector)
    #Normalize vector: important to avoid wrong results when searching
    faiss.normalize_L2(vector)
    #Add to index
    index.add(vector)

def extract_features_clip(image):
    with torch.no_grad():
        inputs = processor_clip(images=image, return_tensors="pt").to(device)
        image_features = model_clip.get_image_features(**inputs)
        return image_features

def extract_features_dino(image):
    with torch.no_grad():
        inputs = processor_dino(images=image, return_tensors="pt").to(device)
        outputs = model_dino(**inputs)
        image_features = outputs.last_hidden_state
        return image_features.mean(dim=1)

#Create 2 indexes.
index_clip = faiss.IndexFlatL2(512)
index_dino = faiss.IndexFlatL2(768)

#Iterate over the dataset to extract features X2 and store features in indexes
for image_path in images:
    img = Image.open(image_path).convert('RGB')
    clip_features = extract_features_clip(img)
    add_vector_to_index(clip_features,index_clip)
    dino_features = extract_features_dino(img)
    add_vector_to_index(dino_features,index_dino)

#store the indexes locally
faiss.write_index(index_clip,"clip.index")
faiss.write_index(index_dino,"dino.index")

第2部分：图像相似度搜索：


import faiss
import numpy as np
import torch
from transformers import AutoImageProcessor, AutoModel, AutoProcessor, CLIPModel
from PIL import Image
import os

#Input image
source='laptop.jpg'
image = Image.open(source)

device = torch.device('cuda' if torch.cuda.is_available() else "cpu")

#Load model and processor DINOv2 and CLIP
processor_clip = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
model_clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)

processor_dino = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
model_dino = AutoModel.from_pretrained('facebook/dinov2-base').to(device)

#Extract features for CLIP
with torch.no_grad():
    inputs_clip = processor_clip(images=image, return_tensors="pt").to(device)
    image_features_clip = model_clip.get_image_features(**inputs_clip)

#Extract features for DINOv2
with torch.no_grad():
    inputs_dino = processor_dino(images=image, return_tensors="pt").to(device)
    outputs_dino = model_dino(**inputs_dino)
    image_features_dino = outputs_dino.last_hidden_state
    image_features_dino = image_features_dino.mean(dim=1)

def normalizeL2(embeddings):
    vector = embeddings.detach().cpu().numpy()
    vector = np.float32(vector)
    faiss.normalize_L2(vector)
    return vector

image_features_dino = normalizeL2(image_features_dino)
image_features_clip = normalizeL2(image_features_clip)

#Search the top 5 images
index_clip = faiss.read_index("clip.index")
index_dino = faiss.read_index("dino.index")

#Get distance and indexes of images associated
d_dino,i_dino = index_dino.search(image_features_dino,5)
d_clip,i_clip = index_clip.search(image_features_clip,5)