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import gradio as gr
import numpy as np
encoded_images = np.load("X_encoded_compressed.npy")
def find_nearest_neighbors(encoded_images, input_image, top_n=5):
"""
Find the closest neighbors to the input image in the encoded image space.
Args:
encoded_images (np.ndarray): Array of encoded images (shape: (n_samples, n_features)).
input_image (np.ndarray): The encoded input image (shape: (1, n_features)).
top_n (int): The number of nearest neighbors to return.
Returns:
List of tuples: (index, distance) of the top_n nearest neighbors.
"""
# Compute pairwise distances
distances = euclidean_distances(encoded_images, input_image.reshape(1, -1)).flatten()
# Sort by distance
nearest_neighbors = np.argsort(distances)[:top_n]
return [(index, distances[index]) for index in nearest_neighbors]
def get_image(index):
split = len(dataset["train"])
if index < split:
return dataset["train"][index]
else:
return dataset["test"][index-split]
def process_image(image):
pass
def inference(image):
input_image = process_image(image)
nearest_neighbors = find_nearest_neighbors(encoded_images, input_image, top_n=5)
# Print the results
print("Nearest neighbors (index, distance):")
for neighbor in nearest_neighbors:
print(neighbor)
top4 = [int(i[0]) for i in nearest_neighbors[:4]]
print(f"top 4: {top4}")
for i in top4:
im = get_image(i)
print(im["label"], im["timestamp"])
n=2
plt.figure(figsize=(8, 8))
for i, (image1, image2) in enumerate(zip(top4[:2], top4[2:])):
ax = plt.subplot(2, n, i + 1)
image1 = get_image(image1)["image"]
image2 = get_image(image2)["image"]
plt.imshow(image1)
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(image2)
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
demo = gr.Interface(fn=greet,
inputs=gr.File(label='Upload image'),
outputs="text")
demo.launch() |