create a random image using numpy 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## random grayscale image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c26510310>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAABbZJREFUeJzt3C9rFWwYx/HriKxY5nAGg0EUx/APNsGmxSbKNIhlweZL0CgYLBrVpmUIazpMggf7BoLFMouIiqBgEPQ8L2EL98Xz8Hs+n3z4nRP25S7jmsxmswIy7fm3fwDQR+AQTOAQTOAQTOAQTOAQTOAQTOAQTOAQbG/H6NLSUsu/x127dq1jtpaXl4dvXr58efhmVdWfP39adre2tlp2X79+3bL79OnT4ZvHjx8fvllVtWdPzzu6vr4+2fG7W74Z+E8QOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOARruap6/fr1jtm6ePFiy+729vbwzffv3w/frKr6+vVry+7Lly9bdm/dutWy+/z58+Gbq6urwzerqg4fPtyyuxtecAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAgmcAg2mc1mw0c3NzfHj1bVdDrtmG25KNpx9bOq6s2bNy27586da9ldXl5u2b158+bwzY8fPw7frKq6ceNGy+758+cnO33GCw7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BWo4unjx5suXo4ocPHzpm68iRI8M3FxcXh29WVX369Kll99u3by27Xccc5+fnh2+urKwM36yqWltba9l99uyZo4vwfyZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCLa3Y/THjx8ds7WxsdGyOzc3N3zz0aNHwzerqqbTacvu2bNnW3Y7rvZWVd25c2f45tGjR4dvVlUdOHCgZXc3vOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQTOAQrOWq6qVLlzpm6+fPny27Bw8eHL759u3b4ZtVVd+/f2/ZPX36dMvuq1evWnb3798/fHPfvn3DN6uqHj9+3LK7G15wCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCCZwCNZyVfXu3bsds7WwsNCyO5lMhm9euXJl+GZV1fb2dsvuiRMnWnZfvHjRsvvly5fhm12XZZ88edKye/Xq1R0/4wWHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYAKHYC1XVQ8dOtQxWw8ePGjZXVlZGb5579694ZtVVevr6y27GxsbLbuLi4stu7dv3x6+ubS0NHyzqurhw4ctu7vhBYdgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgAodgLUcX37171zFbc3NzLbt///4dvnns2LHhm1VVa2trLbubm5stu6urqy27e/eO/9O9cOHC8M2qql+/frXs7oYXHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIIJHIK1XFVdWFjomK379++37G5tbQ3f7Pqtnz9/btk9depUy+5sNmvZnZ+fH745nU6Hb1ZV/f79u2X3zJkzO37GCw7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BBA7BJl1XL4F/nxccggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcggkcgv0DV/qqy6B5jQ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2643dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "random_gimg = np.random.randint(255, size=(10,10)) #512x512 matrix\n",
    "plt.axis('off')\n",
    "plt.imshow(random_gimg, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## random color rgb image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c2672c790>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c242313d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_img = np.random.randint(255, size=(10,10,3))\n",
    "plt.axis('off')\n",
    "plt.imshow(random_img.astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
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 "nbformat_minor": 2
}