{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipfml import processing\n",
    "from ipfml import utils\n",
    "from ipfml import metrics\n",
    "from PIL import Image\n",
    "from scipy import signal\n",
    "from skimage import color\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from pylab import *\n",
    "from skimage import data, io, color\n",
    "\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = \"../fichiersSVD_light\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics analysis on Synthesis Images "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utils functions definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_images_path(dict_data):\n",
    "    \n",
    "    all_images_path = []\n",
    "    for cur_dict in dict_data:\n",
    "        scene = cur_dict['name']\n",
    "        prefix = cur_dict['prefix']\n",
    "        indices = cur_dict['indices']\n",
    "\n",
    "        scene_images_path = []\n",
    "        for index in indices:\n",
    "            path = os.path.join(data_folder, os.path.join(scene, prefix + index + \".png\"))\n",
    "            scene_images_path.append(path)\n",
    "            \n",
    "        all_images_path.append(scene_images_path)\n",
    "            \n",
    "    return all_images_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_sv_data(dict_data, interval, all_images_path):\n",
    "    \n",
    "    sv_values = []\n",
    "    plt.figure(figsize=(25, 20))\n",
    "    begin, end = interval\n",
    "    \n",
    "    for id_dict, cur_dict in enumerate(dict_data):\n",
    "        \n",
    "        scene_name = cur_dict['name']\n",
    "        image_indices = cur_dict['indices']\n",
    "        scene_sv_values = []\n",
    "    \n",
    "        for id_img, img_path in enumerate(all_images_path[id_dict]):\n",
    "            img = Image.open(img_path)\n",
    "            print(img_path)\n",
    "            \n",
    "            blocks = processing.divide_in_blocks(img, (200, 200))\n",
    "            block = np.array(blocks[0])\n",
    "            \n",
    "            if block.ndim == 3:\n",
    "                U, s, V = processing.get_LAB_L_SVD(block)\n",
    "            else:\n",
    "                U, s, V = metrics.get_SVD(block)\n",
    "                \n",
    "            data = s[begin:end]\n",
    "            plt.plot(data, label=scene_name + '_' + str(image_indices[id_img]))\n",
    "            scene_sv_values.append(data)\n",
    "            \n",
    "        sv_values.append(scene_sv_values)\n",
    "\n",
    "    plt.legend(fontsize=18)\n",
    "    plt.show()\n",
    "    return sv_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sv_data(cur_dict, interval, images_path_scene, norm=False):\n",
    "    \n",
    "    scene_name = cur_dict['name']\n",
    "    image_indices = cur_dict['indices']\n",
    "    zone = cur_dict['zone']\n",
    "    scene_sv_values = []\n",
    "    begin, end = interval\n",
    "    \n",
    "    plt.figure(figsize=(25, 15))\n",
    "        \n",
    "    for id_img, img_path in enumerate(images_path_scene):\n",
    "        img = Image.open(img_path)\n",
    "        \n",
    "        img = processing.divide_in_blocks(img, (200, 200))[zone]\n",
    "        \n",
    "        # Convert to L canal\n",
    "        img_grey = np.asarray(metrics.get_LAB_L(img), 'uint8')\n",
    "        \n",
    "        data_new = whiten(img_grey)\n",
    "        data_new = np.diagonal(data_new)\n",
    "        \n",
    "        data = processing.get_LAB_L_SVD_s(img)\n",
    "        \n",
    "        #Image.fromarray(new_img).show()\n",
    "        \n",
    "        if norm:\n",
    "            data_new = utils.normalize_arr(data_new)\n",
    "            data = utils.normalize_arr(data)\n",
    "        \n",
    "        plt.plot(data, label=scene_name + '_' + str(image_indices[id_img] + ' (new)'))\n",
    "        plt.plot(data_new, label=scene_name + '_' + str(image_indices[id_img]))\n",
    "        scene_sv_values.append(data)\n",
    "    \n",
    "    plt.legend(fontsize=18)\n",
    "    plt.show()\n",
    "    return scene_sv_values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def covariance(x):\n",
    "    mean = np.mean(x, axis=1, keepdims=True)\n",
    "    n = np.shape(x)[1] - 1\n",
    "    m = x - mean\n",
    "    return (m.dot(m.T))/n\n",
    "\n",
    "\n",
    "def whiten(x):\n",
    "    # Calculate the covariance matrix\n",
    "    coVarM = covariance(x) \n",
    "\n",
    "    # Singular value decoposition\n",
    "    U, S, V = np.linalg.svd(coVarM)\n",
    "    \n",
    "    # Calculate diagonal matrix of eigenvalues\n",
    "    d = np.diag(1.0 / np.sqrt(S)) \n",
    "   \n",
    "    # Calculate whitening matrix\n",
    "    #whiteM = np.dot(U, np.dot(d, U.T))\n",
    "    \n",
    "    # Project onto whitening matrix\n",
    "    #Xw = np.dot(whiteM, x) \n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenes zones data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# start 00020 - ref 00900 - step 10\n",
    "dict_appart = {'name': 'Appart1opt02', \n",
    "               'prefix': 'appartAopt_', \n",
    "               'indices': [\"00020\", \"00200\", \"00300\", \"00900\"],\n",
    "               'zone': 6}\n",
    "\n",
    "# start 00050 - ref 01200 - step 10\n",
    "dict_cuisine = {'name': 'Cuisine01', \n",
    "               'prefix': 'cuisine01_', \n",
    "               'indices': [\"00050\", \"00400\", \"01200\"],\n",
    "               'zone': 6}\n",
    "\n",
    "# start 00020 - ref 00950 - step 10\n",
    "dict_sdb_c = {'name': 'SdbCentre', \n",
    "               'prefix': 'SdB2_', \n",
    "               'indices': [\"00020\", \"00400\", \"00950\"],\n",
    "               'zone': 6}\n",
    "\n",
    "# start 00020 - ref 00950 - step 10\n",
    "dict_sdb_d = {'name': 'SdbDroite', \n",
    "               'prefix': 'SdB2_D_', \n",
    "               'indices': [\"00020\", \"00400\", \"00950\"],\n",
    "               'zone': 6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dicts = [dict_appart, dict_cuisine, dict_sdb_c, dict_sdb_d]\n",
    "interval = (30, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_path = compute_images_path(all_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene_index = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sv_values = get_sv_data(all_dicts[scene_index], interval, images_path[scene_index], norm=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "thesis-venv",
   "language": "python",
   "name": "thesis-venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}