Update New_file.txt

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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)


	import torch
	from transformers import SwinTransformer, SwinTransformerImageProcessor
	import torchvision.transforms as transforms
	from PIL import Image
	import numpy as np
	from sklearn.metrics.pairwise import cosine_similarity

	# Load the pretrained Swin Transformer model and image processor
	model_name = "microsoft/Swin-Transformer-base-patch4-in22k"
	model = SwinTransformer.from_pretrained(model_name)
	processor = SwinTransformerImageProcessor.from_pretrained(model_name)

	# 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)
	inputs = processor(images=image, return_tensors="pt")
	return inputs

	# Load your ideal and candidate subsets of images
	ideal_image_paths = ["ideal_image1.jpg", "ideal_image2.jpg", "ideal_image3.jpg"] # Replace with your ideal image file paths
	candidate_image_paths = ["candidate_image1.jpg", "candidate_image2.jpg", "candidate_image3.jpg"] # Replace with your candidate image file paths

	# Calculate cosine similarities between ideal and candidate images
	similarities = []

	for ideal_path in ideal_image_paths:
	ideal_embedding = None
	inputs_ideal = preprocess_image(ideal_path)
	with torch.no_grad():
	output_ideal = model(**inputs_ideal)
	ideal_embedding = output_ideal['pixel_values'][0].cpu().numpy()

	for candidate_path in candidate_image_paths:
	candidate_embedding = None
	inputs_candidate = preprocess_image(candidate_path)
	with torch.no_grad():
	output_candidate = model(**inputs_candidate)
	candidate_embedding = output_candidate['pixel_values'][0].cpu().numpy()

	# Calculate cosine similarity between ideal and candidate embeddings
	similarity = cosine_similarity([ideal_embedding], [candidate_embedding])[0][0]
	similarities.append((ideal_path, candidate_path, similarity))

	# Set a similarity threshold (e.g., 0.7)
	threshold = 0.7

	# Find similar image pairs based on the threshold
	similar_pairs = []
	for ideal_path, candidate_path, similarity in similarities:
	if similarity > threshold:
	similar_pairs.append((ideal_path, candidate_path))

	# Print similar image pairs
	for pair in similar_pairs:
	print(f"Similar images: {pair[0]} and {pair[1]}")