AnimationExpt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "76840136",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! convert assets/katakana_a.svg output.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fa5a9ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "cf64d22b",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread(\"assets/katakana_a_white.png\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9776cbe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500, 3)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "f9758a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0ba4542c70>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "5dcb5fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img = np.zeros(image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "932b242c",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bf07d18d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    for x in range(1,499):\n",
    "        for y in range(1,499):\n",
    "            if image[x][y][0] < threshold and (image[x][y-1][0] >threshold or \n",
    "                                        image[x][y+1][0] >threshold or\n",
    "                                        image[x-1][y][0] >threshold or\n",
    "                                        image[x+1][y][0] >threshold):\n",
    "                # make sure not isolated\n",
    "                if (image[x][y-1][0] <threshold or \n",
    "                                        image[x][y+1][0] <threshold or\n",
    "                                        image[x-1][y][0] <threshold or\n",
    "                                        image[x+1][y][0] <threshold):\n",
    "                    new_img[x][y] = [255,255,255]\n",
    "\n",
    "    for x in range(1,499):\n",
    "        for y in range(1,499):\n",
    "            if new_img[x][y][0] == 255:\n",
    "                image[x][y] = [255,255,255]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "1f7d6b05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "78798f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "bb46827b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eb54de9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "c00d66e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0ba4d72cd0>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(new_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c67abaa2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db1999b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}