Update New_file.txt

New_file.txt

@@ -47,3 +47,69 @@ similarities = cosine_similarity(ideal_embeddings, candidate_embeddings)
 
 # Print the similarity matrix
 print(similarities)
+
+
+## SWIN code
+
+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 embeddings for ideal images
+ideal_embeddings = []
+for image_path in ideal_image_paths:
+    inputs = preprocess_image(image_path)
+    with torch.no_grad():
+        output = model(**inputs)
+        embedding = output['pixel_values'][0].cpu().numpy()
+        ideal_embeddings.append(embedding)
+
+# Calculate embeddings for candidate images
+candidate_embeddings = []
+for image_path in candidate_image_paths:
+    inputs = preprocess_image(image_path)
+    with torch.no_grad():
+        output = model(**inputs)
+        embedding = output['pixel_values'][0].cpu().numpy()
+        candidate_embeddings.append(embedding)
+
+# Calculate cosine similarities between ideal and candidate images
+similarities = cosine_similarity(ideal_embeddings, candidate_embeddings)
+
+# Set a similarity threshold (e.g., 0.7)
+threshold = 0.7
+
+# Find similar image pairs based on the threshold
+similar_pairs = []
+for i in range(len(ideal_image_paths)):
+    for j in range(len(candidate_image_paths)):
+        if similarities[i, j] > threshold:
+            similar_pairs.append((ideal_image_paths[i], candidate_image_paths[j]))
+
+# Print similar image pairs
+for pair in similar_pairs:
+    print(f"Similar images: {pair[0]} and {pair[1]}")
+