Update New_file.txt

New_file.txt

@@ -1,112 +1,54 @@
-import torch
-import torchvision.transforms as transforms
-from PIL import Image
+import cv2
 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]}")
 
+# Load the images
+image1_path = 'path_to_your_first_image.jpg'
+image2_path = 'path_to_your_second_image.jpg'
+image1 = cv2.imread(image1_path)
+image2 = cv2.imread(image2_path)
+
+# Resize images to the same height for concatenation
+height1, width1, _ = image1.shape
+height2, width2, _ = image2.shape
+
+# Define the desired height for both images (e.g., height of the first image)
+desired_height = height1
+
+# Resize images
+image1_resized = cv2.resize(image1, (width1, desired_height))
+image2_resized = cv2.resize(image2, (width2, desired_height))
+
+# Combine images side by side
+combined_image = np.hstack((image1_resized, image2_resized))
+
+# Add labels to the top of each image
+label1 = 'Image 1'
+label2 = 'Image 2'
+font = cv2.FONT_HERSHEY_SIMPLEX
+font_scale = 1
+color = (255, 255, 255)  # White color for the text
+thickness = 2
+
+# Calculate the position for the labels
+label1_size = cv2.getTextSize(label1, font, font_scale, thickness)[0]
+label2_size = cv2.getTextSize(label2, font, font_scale, thickness)[0]
+
+# Position for label1
+x1 = 10
+y1 = label1_size[1] + 10
+
+# Position for label2
+x2 = width1 + 10
+y2 = label2_size[1] + 10
+
+# Add labels to the combined image
+cv2.putText(combined_image, label1, (x1, y1), font, font_scale, color, thickness)
+cv2.putText(combined_image, label2, (x2, y2), font, font_scale, color, thickness)
+
+# Display the combined image
+cv2.imshow('Combined Image', combined_image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+# Save the combined image
+cv2.imwrite('combined_image.jpg', combined_image)