Merge branch 'release/v0.4.8'

analysis/svd_entropy_analysis_convergence.ipynb

@@ -0,0 +1,394 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "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",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_folder = \"../fichiersSVD_light\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SVD analysis on zones of 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",
+    "    scene = dict_data['name']\n",
+    "    prefix = dict_data['prefix']\n",
+    "    indices = dict_data['indices']\n",
+    "    \n",
+    "    images_path = []\n",
+    "    for index in indices:\n",
+    "        path = os.path.join(data_folder, os.path.join(scene, prefix + index + \".png\"))\n",
+    "        print(path)\n",
+    "        images_path.append(path)\n",
+    "    return images_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_images_zones(dict_data, images_path):\n",
+    "    \n",
+    "    zones_indices = dict_data['zones']\n",
+    "    zones_img = []\n",
+    "    \n",
+    "    for path in images_path:\n",
+    "        img = Image.open(path)\n",
+    "        zones = processing.divide_in_blocks(img, (200, 200))\n",
+    "        \n",
+    "        zones_list = []\n",
+    "        \n",
+    "        for id_zone in zones_indices:\n",
+    "            zones_list.append(zones[id_zone])\n",
+    "            \n",
+    "        zones_img.append(zones_list)\n",
+    "        \n",
+    "    return zones_img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def display_sv_data(dict_data, zones_data, interval, _norm=False):\n",
+    "    \n",
+    "    scene_name = dict_data['name']\n",
+    "    image_indices = dict_data['indices']\n",
+    "    zones_indices = dict_data['zones']\n",
+    "    colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9']\n",
+    "    \n",
+    "    plt.figure(figsize=(25, 20))\n",
+    "    \n",
+    "    sv_data = []\n",
+    "    begin, end = interval\n",
+    "    for id_img, zones in enumerate(zones_data):\n",
+    "        \n",
+    "        for id_zone, zone in enumerate(zones):\n",
+    "            U, s, V = processing.get_LAB_L_SVD(zone)\n",
+    "        \n",
+    "            data = s[begin:end]\n",
+    "            \n",
+    "            if _norm:\n",
+    "                data = utils.normalize_arr(data)\n",
+    "                \n",
+    "            plt.plot(data, \n",
+    "                     color=colors[id_zone], \n",
+    "                     label='Zone ' + str(zones_indices[id_zone]) + ' of ' + scene_name + '_' + str(image_indices[id_img]))\n",
+    "            \n",
+    "    plt.legend(fontsize=18)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Useful function\n",
+    "\n",
+    "def get_highest_values(arr, n):\n",
+    "    return np.array(arr).argsort()[-n:][::-1]\n",
+    "\n",
+    "def get_lowest_values(arr, n):\n",
+    "    return np.array(arr).argsort()[::-1][-n:][::-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_entropy(arr):\n",
+    "    arr = np.array(arr)\n",
+    "    eigen_values = []\n",
+    "    sum_eigen_values = (arr * arr).sum()\n",
+    "    print(sum_eigen_values)\n",
+    "\n",
+    "    for id, val in enumerate(arr):\n",
+    "        eigen_values.append(val * val)\n",
+    "        #print(id, \" : \", val)\n",
+    "\n",
+    "    v = []\n",
+    "\n",
+    "    for val in eigen_values:\n",
+    "        v.append(val / sum_eigen_values)\n",
+    "\n",
+    "    entropy = 0\n",
+    "\n",
+    "    for val in v:\n",
+    "        if val > 0:\n",
+    "            entropy += val * math.log(val)\n",
+    "\n",
+    "    entropy *= -1\n",
+    "\n",
+    "    entropy /= math.log(len(v))\n",
+    "    \n",
+    "    return entropy\n",
+    "\n",
+    "\n",
+    "def get_entropy_without_i(arr, i):\n",
+    "    \n",
+    "    arr = np.array([v for index, v in enumerate(arr) if index != i])\n",
+    "\n",
+    "    return get_entropy(arr)\n",
+    "\n",
+    "def get_entropy_contribution_of_i(arr, i):\n",
+    "\n",
+    "    return get_entropy(arr) - get_entropy_without_i(arr, i)"
+   ]
+  },
+  {
+   "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\", \"00900\"],\n",
+    "               'zones': [3, 6]}\n",
+    "\n",
+    "# start 00050 - ref 01200 - step 10\n",
+    "dict_cuisine = {'name': 'Cuisine01', \n",
+    "               'prefix': 'cuisine01_', \n",
+    "               'indices': [\"00050\", \"00400\", \"01200\"],\n",
+    "               'zones': [3, 6]}\n",
+    "\n",
+    "# start 00020 - ref 00950 - step 10\n",
+    "dict_sdb_c = {'name': 'SdbCentre', \n",
+    "               'prefix': 'SdB2_', \n",
+    "               'indices': [\"00020\", \"00400\", \"00950\"],\n",
+    "               'zones': [3, 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",
+    "               'zones': [2, 3, 10, 13]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "current_dict = dict_appart\n",
+    "interval = (30, 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../fichiersSVD_light/Appart1opt02/appartAopt_00020.png\n",
+      "../fichiersSVD_light/Appart1opt02/appartAopt_00200.png\n",
+      "../fichiersSVD_light/Appart1opt02/appartAopt_00900.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "images_path = compute_images_path(current_dict)\n",
+    "zones_data = get_images_zones(current_dict, images_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1277393.7121246634\n",
+      "1277393.7121246634\n",
+      "0 :  0.7291941465931915\n"
+     ]
+    }
+   ],
+   "source": [
+    "first_image = zones_data[0][0]\n",
+    "# first_image = metrics.get_LAB_L(first_image)\n",
+    "\n",
+    "# print(first_image[0:2, 0:2])\n",
+    "# Image.fromarray(first_image).show()\n",
+    "\n",
+    "# first_image = np.asarray(Image.fromarray(first_image).convert('L'))\n",
+    "#first_image.show()\n",
+    "\n",
+    "entropy_contribution_data = []\n",
+    "\n",
+    "sv = processing.get_LAB_L_SVD_s(first_image)\n",
+    "# sv = utils.normalize_arr(sv)\n",
+    "#entropy = get_entropy(sv)\n",
+    "\n",
+    "#for i in range(200):\n",
+    "entropy_contribution_data.append(get_entropy_without_i(sv, 0))\n",
+    "print(0, \": \", get_entropy_without_i(sv, 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[  0   0   0]\n",
+      "  [  0   0   0]]\n",
+      "\n",
+      " [[164 152 143]\n",
+      "  [159 144 132]]]\n",
+      "[87.9761409  0.       ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "sub_blocks = processing.divide_in_blocks(first_image, (2,2))\n",
+    "sub_block = np.asarray(sub_blocks[0])\n",
+    "sub_block\n",
+    "\n",
+    "sv_values = processing.get_LAB_L_SVD_s(sub_block)\n",
+    "print(sub_block)\n",
+    "print(sv_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0, 199, 198, 197, 196, 195, 194, 193, 192, 191, 190, 189, 188,\n",
+       "       187, 186, 185, 184, 183, 182, 181, 180, 179, 178, 177, 176, 175,\n",
+       "       174, 173, 172, 171, 170, 169, 168, 167, 166, 165, 164, 163, 162,\n",
+       "       161, 160, 159, 158, 157, 156, 155, 154, 153, 152, 151, 150, 149,\n",
+       "       148, 147, 146, 145, 144, 143, 142, 141, 140, 139, 138, 137, 136,\n",
+       "       135, 134, 133, 132, 131, 130, 129, 128, 127, 126, 125, 124, 123,\n",
+       "       122, 121, 120, 119, 118, 117, 116, 115, 114, 113, 112, 111, 110,\n",
+       "       109, 108, 107, 106, 105, 104, 103, 102, 101])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_highest_values(entropy_contribution_data, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
+       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
+       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
+       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
+       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
+       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
+       "        92,  93,  94,  95,  96,  97,  98,  99, 100])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_lowest_values(entropy_contribution_data, 100)"
+   ]
+  }
+ ],
+ "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
+}

modules

@@ -1 +1 @@
-Subproject commit c500a84bebabfbebc53041bf8a00f0120cd417c3
+Subproject commit 270de3a969ff3121e68f435cc6a3b570ba5b9d69