New_file.txt

New_file.txt

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+import torch
+from transformers import SwinTransformer, SwinTransformerTokenizer
+import torchvision.transforms as transforms
+from PIL import Image
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load the pre-trained Swin Transformer model and tokenizer
+model_name = "microsoft/Swin-Transformer-base-patch4-in22k"
+model = SwinTransformer.from_pretrained(model_name)
+tokenizer = SwinTransformerTokenizer.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)
+    image = transform(image).unsqueeze(0)  # Add a batch dimension
+    return image
+
+# Load your ideal subset of images
+ideal_image_paths = ["ideal_image1.jpg", "ideal_image2.jpg", "ideal_image3.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():
+        input_ids = tokenizer(image_path, return_tensors="pt").input_ids
+        embedding = model.pixel_values(input_ids).numpy()
+        ideal_embeddings.append(embedding)
+
+# Load a set of candidate images
+candidate_image_paths = ["candidate_image1.jpg", "candidate_image2.jpg", "candidate_image3.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():
+        input_ids = tokenizer(image_path, return_tensors="pt").input_ids
+        embedding = model.pixel_values(input_ids).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)