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import torch
import torchvision.transforms as transforms
from PIL import Image
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from torchvision.models import resnet50
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
# Load a pre-trained ResNet-50 model
model = resnet50(pretrained=True)
model.eval()
# Define a function to preprocess images
def preprocess_image(image_path):
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
image = Image.open(image_path)
image = transform(image).unsqueeze(0) # Add a batch dimension
return image
# Load your ideal subset of images
ideal_image_paths = ["/content/trunck.jpg", "t4.jpg"] # Replace with your ideal image file paths
ideal_embeddings = []
for image_path in ideal_image_paths:
image = preprocess_image(image_path)
with torch.no_grad():
embedding = model(image).squeeze().numpy()
ideal_embeddings.append(embedding)
# Load a set of candidate images
candidate_image_paths = ["/content/trunck2.jpg", "t3.jpg", "car.jpg",] # Replace with your candidate image file paths
candidate_embeddings = []
for image_path in candidate_image_paths:
image = preprocess_image(image_path)
with torch.no_grad():
embedding = model(image).squeeze().numpy()
candidate_embeddings.append(embedding)
# Calculate similarities between ideal and candidate images using cosine similarity
similarities = cosine_similarity(ideal_embeddings, candidate_embeddings)
# Print the similarity matrix
print(similarities)
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