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import torch
import torchvision.transforms as transforms
from PIL import Image
from sklearn.metrics.pairwise import cosine_similarity
import timm
import numpy as np
import gradio as gr
class ImageEmbedder:
def __init__(self, model_name='vit_base_patch16_224'):
self.model = timm.create_model(model_name, pretrained=True)
self.model.head = torch.nn.Identity() # Remove classification head
self.model.eval()
self.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])
])
def get_embedding(self, image):
image = image.convert('RGB')
image_tensor = self.transform(image).unsqueeze(0)
with torch.no_grad():
embedding = self.model(image_tensor)
return embedding.squeeze().numpy()
def compare_images(image1, image2, similarity_threshold=0.85):
embedder = ImageEmbedder()
# Get embeddings
embedding1 = embedder.get_embedding(image1)
embedding2 = embedder.get_embedding(image2)
# Calculate similarity
similarity = cosine_similarity(embedding1.reshape(1, -1), embedding2.reshape(1, -1))[0][0]
# Determine if images are similar
if similarity > similarity_threshold:
return f"The images are similar. Similarity score: {similarity:.4f}"
else:
return f"The images are not similar. Similarity score: {similarity:.4f}"
def main(image1, image2):
return compare_images(image1, image2)
iface = gr.Interface(
fn=main,
inputs=[gr.Image(type="pil"), gr.Image(type="pil")],
outputs="text",
title="Image Similarity Checker",
description="Upload two images to check their similarity based on embeddings."
)
iface.launch()
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