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{ "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": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2, 59520)\n" ] } ], "source": [ "# load figure as 2D array \n", "data = np.load('horse.npy')\n", "print(data.shape)" ] }, { "cell_type": "code", "execution_count": 3, "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": 4, "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": 5, "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": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-1.2, 1.2, -1.2, 1.2)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1000x1000 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "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}
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