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python opencv shrink interpolation comparsion
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import cv2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "a = np.zeros((100,100))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "for i in range(50):\n", | |
| " a[:][i] = 1\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24ed7699c88>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.imshow(a, cmap='gray')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMnypIcOXGjJ8qSHDlxoyfKmh/wE6lRkU78/0SQAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24ed773bbe0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "b = cv2.resize(a, (50, 50), interpolation=cv2.INTER_AREA)\n", | |
| "plt.imshow(b, cmap='gray')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 41, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24ed781a3c8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "b = cv2.resize(a, (50, 50), interpolation=cv2.INTER_LANCZOS4)\n", | |
| "plt.imshow(b, cmap='gray')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 42, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMnypIcOXGjJ8qSHDlxoyfKmh/wE6lRkU78/0SQAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24ed76b06d8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "b = cv2.resize(a, (50, 50), interpolation=cv2.INTER_LINEAR)\n", | |
| "plt.imshow(b, cmap='gray')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 43, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24ed779ecc0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "b = cv2.resize(a, (50, 50), interpolation=cv2.INTER_NEAREST)\n", | |
| "plt.imshow(b, cmap='gray')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "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": "3.5.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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