134 lines
3.1 KiB
Plaintext
134 lines
3.1 KiB
Plaintext
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Exercise 1b: Read a binary file which contains pixel data and apply\n",
|
||
|
|
"transformations"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import numpy as np\n",
|
||
|
|
"import matplotlib.pyplot as plt"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# load figure as 2D array \n",
|
||
|
|
"data = np.load('horse.npy')\n",
|
||
|
|
"print(data.shape)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# just scale the data by a factor and shift by trans\n",
|
||
|
|
"trans = np.ones(data.shape)\n",
|
||
|
|
"trans[0,:] *=0.6\n",
|
||
|
|
"trans[1,:] *=0.4\n",
|
||
|
|
"factor = 0.5 \n",
|
||
|
|
"data_scale = data * factor + trans"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#compression in x and y \n",
|
||
|
|
"sx = 0.4\n",
|
||
|
|
"sy = 0.9\n",
|
||
|
|
"t = np.array([[sx,0],[0,sy]])\n",
|
||
|
|
"data_comp = t@data"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#rotation by an angle theta\n",
|
||
|
|
"theta = 0.5\n",
|
||
|
|
"data_rot = np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta), np.cos(theta)]])@data"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#spiegelung an der x Achse\n",
|
||
|
|
"tx = np.array([[1,0],[0,-1]]) # mirror x axis\n",
|
||
|
|
"ty = np.array([[-1,0],[0,1]]) # mirror y axis\n",
|
||
|
|
"tp = np.array([[-1,0],[0,-1]]) # mirror (0,0)\n",
|
||
|
|
"data_mirror = tp@data"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# create figure for the transformations\n",
|
||
|
|
"plt.figure(figsize=(10.0,10.0),dpi=100,facecolor='lightgrey')\n",
|
||
|
|
"plt.suptitle('Plot Transformations')\n",
|
||
|
|
"plt.subplot(2,2,1)\n",
|
||
|
|
"plt.title('original picture')\n",
|
||
|
|
"plt.plot(data[0,:],data[1,:],'.')\n",
|
||
|
|
"plt.axis([-1.2,1.2,-1.2,1.2])\n",
|
||
|
|
"plt.subplot(2,2,2)\n",
|
||
|
|
"plt.title('scaling and translation')\n",
|
||
|
|
"plt.plot(data_scale[0,:],data_scale[1,:],'.')\n",
|
||
|
|
"plt.axis([-1.2,1.2,-1.2,1.2])\n",
|
||
|
|
"plt.subplot(2,2,3)\n",
|
||
|
|
"plt.title('compression')\n",
|
||
|
|
"plt.plot(data_comp[0,:],data_comp[1,:],'.')\n",
|
||
|
|
"plt.axis([-1.2,1.2,-1.2,1.2])\n",
|
||
|
|
"plt.subplot(2,2,4)\n",
|
||
|
|
"plt.title('rotation and mirror at p(0,0)')\n",
|
||
|
|
"plt.plot(data_rot[0,:],data_rot[1,:],'.')\n",
|
||
|
|
"plt.plot(data_mirror[0,:],data_mirror[1,:],'.')\n",
|
||
|
|
"plt.axis([-1.2,1.2,-1.2,1.2])"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|