Writing triskele wrapper in Notebook

Notebooks/TRYSKELE Wrapper.ipynb

@@ -1,11 +1,153 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TRYSKELE Wrapper\n",
+    "\n",
+    "Dependence \n",
+    "--\n",
+    "\n",
+    "`tryskele` project folder (compiled) in jupyter's root directory. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from pathlib import Path\n",
+    "import subprocess\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triskele_bin = Path('../triskele/build/out/apGenerator')\n",
+    "triskele_bin.exists()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "process = [triskele_bin.absolute(),\n",
+    "          '']\n",
+    "\n",
+    "tryskele = subprocess.Popen(process, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
+    "print('STDOUT:\\n' + tryskele.stdout.read().decode() + '\\nSTDERR:\\n' + tryskele.stderr.read().decode())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "area = np.array([42,100,1000])\n",
+    "np.savetxt(area_conf, area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Files\n",
+    "infile  = Path('../Data/phase1_rasters/DEM_C123_TLI/UH17_GEG05_TR.tif')\n",
+    "outfile = Path('../Res/out.tiff')\n",
+    "\n",
+    "area_conf      = Path('../Res/area.txt')\n",
+    "deviation_conf = Path('../Res/deviation.txt')\n",
+    "inertia_conf   = Path('../Res/inertia.txt')\n",
+    "\n",
+    "# Attributes definition\n",
+    "area      = np.arange(100)#np.array([10,100,1000])\n",
+    "deviation = np.array([10,100,1000])\n",
+    "inertia   = np.array([10,100,1000])\n",
+    "np.savetxt(area_conf, area, fmt='%d')\n",
+    "np.savetxt(deviation_conf, deviation, fmt='%d')\n",
+    "np.savetxt(inertia_conf, inertia, fmt='%d')\n",
+    "\n",
+    "process = [triskele_bin.absolute(),\n",
+    "           '-i', infile.absolute(),\n",
+    "           '--tos-tree',\n",
+    "           '--area', area_conf.absolute(),\n",
+    "           '--standard-deviation', deviation_conf.absolute(),\n",
+    "           '--moment-of-inertia', inertia_conf.absolute(),\n",
+    "           '-o', outfile.absolute()]\n",
+    "\n",
+    "tryskele = subprocess.Popen(process, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
+    "print('STDOUT:\\n' + tryskele.stdout.read().decode() + '\\nSTDERR:\\n' + tryskele.stderr.read().decode())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.expand_dims(out[:,:,0] < 50, axis=2).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out = plt.imread(outfile.absolute())\n",
+    "display(out.shape)\n",
+    "\n",
+    "dwidth = 32\n",
+    "plt.figure(figsize=(dwidth, dwidth * out.shape[0] / out.shape[1] * .80))\n",
+    "#plt.figure(figsize=(16,9))\n",
+    "\n",
+    "display_channel = 2\n",
+    "plt.imshow(out[:,:,display_channel] * (out[:,:,display_channel] < 50))\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_df = pd.DataFrame(out.reshape(-1, out.shape[2]))\n",
+    "out_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.unique(out_df, return_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(out_df[0] * out_df[0] , 256, log=True)\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {