{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "triskele_path = Path('../triskele/python/')\n",
    "sys.path.append(str(triskele_path.resolve()))\n",
    "import triskele\n",
    "\n",
    "# Specific Utils\n",
    "\n",
    "def DFC_filter(raster):\n",
    "    raster[raster > 1e4] = raster[raster < 1e4].max()\n",
    "\n",
    "def show(im, im_size=1, save=None):\n",
    "    plt.figure(figsize=(16*im_size,3*im_size))\n",
    "    plt.imshow(im)\n",
    "    plt.colorbar()\n",
    "    \n",
    "    if save is not None:\n",
    "        plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "def mshow(Xs, titles=None, im_size=1, save=None):\n",
    "    s = len(Xs)\n",
    "\n",
    "    plt.figure(figsize=(16*im_size,3*im_size*s))\n",
    "\n",
    "    for i in range(s):\n",
    "        plt.subplot(s,1,i+1)\n",
    "        plt.imshow(Xs[i])\n",
    "        \n",
    "        if titles is not None:\n",
    "            plt.title(titles[i])\n",
    "            \n",
    "        plt.colorbar()\n",
    "    \n",
    "    if save is not None:\n",
    "        plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernel Density Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raster = triskele.read('../Data/test.tiff')\n",
    "show(raster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = stats.gaussian_kde(raster.reshape(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "test = cv2.imread('/home/florent/Pictures/Jura-Panorama.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [x for x in range(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel.pdf(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(bins, kernel.pdf(bins))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kB = stats.gaussian_kde(test[:,:,0].reshape(-1))\n",
    "kG = stats.gaussian_kde(test[:,:,1].reshape(-1))\n",
    "kR = stats.gaussian_kde(test[:,:,2].reshape(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [x for x in range(255)]\n",
    "plt.plot(bins, kB.pdf(bins))\n",
    "plt.plot(bins, kG.pdf(bins))\n",
    "plt.plot(bins, kR.pdf(bins))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [x for x in range(10)]\n",
    "plt.plot(bins, kB.pdf(bins))\n",
    "plt.plot(bins, kG.pdf(bins))\n",
    "plt.plot(bins, kR.pdf(bins))\n",
    "plt.show()"
   ]
  }
 ],
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