Machine Learning Kurs im Rahmen der Studierendentage im SS 2023
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Exercise 1: Create numpy array and draw rgb color objects"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"create data array 2x2 as pixel position and 1x3 as rgb color data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"width, height = 200, 200\n",
"data = np.zeros((height, width, 3), dtype=np.uint8)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"draw blue cross"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"x = np.arange(width)\n",
"x_1 = np.arange(width)\n",
"x_2 = np.arange(width-1,-1,-1)\n",
"y = np.arange(height)\n",
"data[x_1,y] = [0,0,255]\n",
"data[x_2,y] = [0,0,255]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" draw a square "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"lower = 55\n",
"upper = 75\n",
"data[lower:upper,lower:upper] = [0,255,0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"create a mask of a circle using indexing\n",
"np.newaxis adds another dimension\n",
"we create a row and column vector and fill it using the condition"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"x_center = 100\n",
"y_center = 100\n",
"radius = 10\n",
"mask = (x[np.newaxis,:]-x_center)**2 + (y[:,np.newaxis]-y_center)**2 < radius**2\n",
"data[mask] = [255,0,0]\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot image\n",
"plt.figure(figsize=(4.,4.),dpi=100,facecolor='lightgrey')\n",
"plt.imshow(data)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.16"
}
},
"nbformat": 4,
"nbformat_minor": 4
}