{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
ESF projekt Západočeské univerzity v Plzni reg. č. CZ.02.2.69/0.0/0.0/16 015/0002287
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Object Description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Popis objektu - cílem je nalézt popis objektu, jež dostatečně vyjadřuje jeho vlastnosti. Použit následně pro klasifikátor.\n", "\n", "Barvení oblastí - ekvalizace barev (kolize při obarvení).\n", "\n", "Freemanův řetězový kod - 4 okolí, 8 okolí\n", "\n", "Popis oblasti - velikost, pravoúhlost, podlouhlost\n", "\n", "Velikost - Dána počtem obrazových elementů\n", "\n", "Eulerovo cislo - E = S (počet souvislých částí) - N (počet děr)\n", "Výška, Šířka\n", "\n", "Výstřednost - poměr nejdelší tětivy a k ní nejdelší kolem tětivy\n", "\n", "Podlouhlost - poměr mezi délkou a šířkou pravoúhelníku opsaného oblasti, která má nejmenší plochu ze všech pravoúhelníků, které lze oblasti opsat\n", "\n", "Pravoúhlost - poměr mezi velikostí oblasti a plochou opsaného pravoúhelníka ve směru natočení\n", "\n", "Směr - smysl pro podlouhle oblasti\n", "\n", "Nekompaktnost - (délka_hranice_oblasti)na druhou/ velikost\n", "\n", "Konvexní obal - nejmenší opsaná konvexní oblast" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import skimage\n", "import skimage.io\n", "import skimage.morphology\n", "import skimage.measure\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "imrgb = skimage.io.imread('http://www.kky.zcu.cz/uploads/courses/zdo/lesson7/1.jpg')\n", "im = skimage.color.rgb2gray(imrgb) > 0.5\n", "\n", "imlabel0 = skimage.measure.label(im)\n", "\n", "imlabel1 = skimage.measure.label(im, background=0)\n", "\n", "plt.subplot(131)\n", "plt.title('orig')\n", "plt.imshow(im, cmap='gray')\n", "plt.subplot(132)\n", "plt.title('label')\n", "plt.imshow(imlabel0, cmap='gray')\n", "plt.subplot(133)\n", "plt.title('label\\n defined backgr.')\n", "plt.imshow(imlabel1, cmap='gray')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels [0 1 2]\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"labels \", np.unique(imlabel1))\n", "\n", "plt.subplot(131)\n", "plt.title('Object 0')\n", "plt.imshow(imlabel1==0, cmap='gray')\n", "plt.subplot(132)\n", "plt.title('Object 1')\n", "plt.imshow(imlabel1==1, cmap='gray')\n", "plt.subplot(133)\n", "plt.title('Object 2')\n", "plt.imshow(imlabel1==2, cmap='gray')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Centroid: (62.486293379994166, 84.67264508603091)\n", "Area: 6858\n", "Euler: -1\n", "Perimeter: 686.4823227814082\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "props = skimage.measure.regionprops(imlabel1)\n", "object_number = 0\n", "print(\"Centroid: \", props[object_number].centroid)\n", "print(\"Area: \", props[object_number].area)\n", "print (\"Euler: \", props[object_number].euler_number)\n", "print (\"Perimeter: \", props[object_number].perimeter)\n", "ci = props[object_number].convex_image\n", "plt.imshow(ci)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Centroid: (113.5, 146.51309408341416)\n", "Area: 2062\n", "Euler: 1\n", "Perimeter: 179.19595949289334\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "props = skimage.measure.regionprops(imlabel1)\n", "\n", "object_number = 1\n", "print(\"Centroid: \", props[object_number].centroid)\n", "print (\"Area: \", props[object_number].area)\n", "print (\"Euler: \", props[object_number].euler_number)\n", "print (\"Perimeter: \", props[object_number].perimeter) \n", "ci = props[object_number].convex_image\n", "plt.imshow(ci)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Id: 0\n", "Centroid: (62.486293379994166, 84.67264508603091)\n", "Area: 6858\n", "Euler: -1\n", "\n", "Id: 1\n", "Centroid: (113.5, 146.51309408341416)\n", "Area: 2062\n", "Euler: 1\n", "\n" ] } ], "source": [ "for i in range(len(props)):\n", " print(\"Id: \", i)\n", " print(\"Centroid: \", props[i].centroid)\n", " print(\"Area: \", props[i].area)\n", " print(\"Euler: \", props[i].euler_number)\n", " print (\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bez pomoci props naprogramujte skript, ktery spocte plochu a obvod. Vykresli objekty s nejvetsim obvodem a nejvetsi plochou." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 1 }