ld2daps/Notebooks/DFC2018 Ground truth split train and validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Triskele\n",
    "import sys\n",
    "from pathlib import Path\n",
    "triskele_path = Path('../triskele/python/')\n",
    "sys.path.append(str(triskele_path.resolve()))\n",
    "import triskele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = np.array([16,3]) * 2  # Figure Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load ground truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt = triskele.read('../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=fs)\n",
    "plt.imshow(gt)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = np.unique(gt, return_counts=True)\n",
    "lbl = pd.read_csv('../Data/ground_truth/labels.csv', header=None, names=['Label'], index_col=0)\n",
    "lbl['Count'] = count[1]\n",
    "lbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar(*count, log=True)\n",
    "plt.xticks(lbl.index.values,lbl['Label'], rotation=90)\n",
    "plt.show()\n",
    "\n",
    "plt.bar(count[0][1:], count[1][1:])\n",
    "plt.xticks(lbl.index.values[1:], lbl['Label'][1:], rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data\n",
    "\n",
    "Create a filter raster `split` with values:\n",
    "\n",
    "- `0` No Data\n",
    "- `1` Train\n",
    "- `2` Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_part = .9\n",
    "\n",
    "split = np.zeros_like(gt)\n",
    "\n",
    "for lbl, lblc in zip(count[0][1:], count[1][1:]):\n",
    "    treshold = int(lblc * train_part)\n",
    "    #print('lbl:{}, count:{}, train:{}'.format(lbl, lblc, treshold))\n",
    "    f = np.nonzero(gt == lbl)\n",
    "    split[f[0][:treshold], f[1][:treshold]] = 1\n",
    "    split[f[0][treshold:], f[1][treshold:]] = 2\n",
    "    \n",
    "plt.figure(figsize=fs)\n",
    "plt.imshow(split)\n",
    "#plt.imshow(split * (gt==1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation\n",
    "import numpy as np\n",
    "\n",
    "t = np.linspace(0,2*np.pi)\n",
    "x = np.sin(t)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.axis([0,2*np.pi,-1,1])\n",
    "l, = ax.plot([],[])\n",
    "\n",
    "def animate(i):\n",
    "    l.set_data(t[:i], x[:i])\n",
    "\n",
    "ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))\n",
    "\n",
    "from IPython.display import HTML\n",
    "HTML(ani.to_jshtml())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metaclasses "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metaclasses_A = {'Grass': [1, 2, 3],\n",
    "                 'Trees': [4, 5],\n",
    "                 'Hard Ground': [6, 17],\n",
    "                 'Water': [7],\n",
    "                 'Buildings': [8, 9],\n",
    "                 'Roads': [10, 11, 12, 13, 14, 16],\n",
    "                 'Railways': [15],\n",
    "                 'Cars': [18],\n",
    "                 'Trains': [19],\n",
    "                 'Stadium': [20]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=fs)\n",
    "plt.imshow(np.isin(gt, metaclasses_A['Roads']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=fs)\n",
    "plt.imshow(gt * np.isin(gt, [10, 11]))#metaclasses_A['Roads']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~~on~~"
   ]
  }
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