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