analyze.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('output.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "map = np.zeros([201, 201]) # 10m x 10m with resolution 10/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in data.iterrows():\n",
    "    x = row[1][0]\n",
    "    y = row[1][1]\n",
    "    ix = int(x / 0.1 + 100)\n",
    "    iy = int(100 - y / 0.1)\n",
    "    map[iy-3:iy+3][ix-3:ix+3] = 1\n",
    "map[99:101, 99:101] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6db43cc3d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(map, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0]), array([0]))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2 \n",
    "def generate_inflation_map(img):\n",
    "    curr_map = (img > 0).astype(np.uint8)\n",
    "    curr_map = 1 - np.clip(curr_map, 0, 1)\n",
    "\n",
    "    curr_map = cv2.distanceTransform(curr_map, cv2.DIST_L2, cv2.DIST_MASK_PRECISE)\n",
    "\n",
    "    new_curr = -1.0 * cost_scaling_factor * (curr_map * 10/128 - inscribed_radius)\n",
    "    costs = np.power(np.e, new_curr) * 253\n",
    "    costs = np.clip(costs, 0, 255).astype(np.uint8)\n",
    "\n",
    "    path = PATH + \"inflated/\" + filename.split(\"/\")[-2] + \"/\"\n",
    "    create_dir_if_not_exist(path)\n",
    "    name = filename.split(\"/\")[-1][:-4]\n",
    "    cv2.imwrite(path + name+ \".png\", costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "codebotler",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}