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text_image_search.py
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text_image_search.py
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import clip
import matplotlib.pyplot as plt
import torch
from scrapper import scrape_images
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)
image_database = scrape_images()
image_database_processed = [
preprocess(im) for im in image_database
] # preprocess each Image
with torch.no_grad():
database_embeddings = model.encode_image(
torch.stack(image_database_processed)
) # Torch.Stack will help us to levragebatch processing to speed up the calculation
def text_image_search(query_text: str, database_embeddings: torch.Tensor):
query_embeddings = model.encode_text(clip.tokenize([query_text]).to(device))
similariries = query_embeddings @ database_embeddings.T
return similariries
if __name__ == "__main__":
query = "A photo of Apple"
sim = text_image_search(query, database_embeddings)
sim_dict = dict(
zip(range(len(sim[0])), sim[0])
) # Use Dictionary to Sort the Results
sorted_sim = sorted(sim_dict.items(), key=lambda x: x[1], reverse=True)
top_sim = sorted_sim[:6] # Get top 6 results
fig, axs = plt.subplots(2, 3, figsize=(15, 6), facecolor="w", edgecolor="k")
fig.subplots_adjust(hspace=0.5, wspace=0.001)
axs = axs.ravel()
fig.suptitle(f"Text - Image Search: \nQuery: {query}")
for num, i in enumerate(top_sim):
axs[num].imshow(image_database[i[0]])
plt.show()