{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Eigenface"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.datasets import fetch_olivetti_faces"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400, 64, 64)\n"
]
}
],
"source": [
"# Step 1: Load Olivetti Faces dataset\n",
"faces_data = fetch_olivetti_faces(shuffle=True)\n",
"images = faces_data.images # Shape (400, 64, 64)\n",
"targets = faces_data.target # Labels (0-39, each person has 10 images)\n",
"\n",
"print(images.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"PCA(n_components=5) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 8))\n",
"show_images = np.concatenate((pca.components_, person_images), axis=0) \n",
"# Step 3: Visualize Eigenfaces\n",
"for j in range(show_images.shape[0]):\n",
" plt.subplot(3, n_components, j + 1)\n",
" plt.imshow(show_images[j].reshape(64, 64), cmap='gray')\n",
" plt.axis('off')\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}