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image_image_search.py
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image_image_search.py
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from typing import List
import clip
import cv2
import matplotlib.pyplot as plt
import torch
from PIL import Image
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 image_image_search(query_image: Image, database_embeddings: torch.Tensor):
query_embeddings = model.encode_image(
preprocess(query_image).unsqueeze(0).to(device)
)
similariries = query_embeddings @ database_embeddings.T
return similariries
if __name__ == "__main__":
image_np = cv2.imread("data/query_apple.jpeg")
image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image_np)
sim = image_image_search(image, 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(3, 3, figsize=(15, 6), facecolor="w", edgecolor="k")
fig.subplots_adjust(hspace=0.5, wspace=0.001)
plt.title("Image - Image Search Results")
axs = axs.ravel()
axs[0].imshow(image_np)
axs[0].set_title("Query Image")
axs[0].axes.xaxis.set_ticklabels([])
axs[0].axes.yaxis.set_ticklabels([])
for num, i in enumerate(top_sim, start=1):
axs[num].imshow(image_database[i[0]])
axs[num].set_title(f"Similarity: {i[1]:.2f}")
axs[num].axes.xaxis.set_ticklabels([])
axs[num].axes.yaxis.set_ticklabels([])
fig.delaxes(axs[-1])
fig.delaxes(axs[-2])
plt.show()