{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Resize the data\n",
    "\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import os, sys\n",
    "path = \"/Users/vasaini/MLHACK/orig\"\n",
    "dirs = os.listdir( path )\n",
    "def resize():\n",
    "    for item in dirs:\n",
    "        if os.path.isfile(path+item):\n",
    "            im = Image.open(path+item)\n",
    "            f, e = os.path.splitext(path+item)\n",
    "            imResize = im.resize ((128,128),\"/Users/vasaini/MLHACK/resized\")\n",
    "                       \n",
    "resize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data as np array\n",
    "\n",
    "import os\n",
    "for file in os.listdir(r\"/Users/vasaini/MLHACK/resized\"):\n",
    "    img = Image.open(os.path.join(r'/Users/vasaini/MLHACK/resized', file))\n",
    "    data = np.array(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['axes', 'boots', 'carabiners', 'crampons', 'gloves', 'hardshell_jackets', 'harnesses', 'helmets', 'insulated_jackets', 'pulleys', 'rope', 'tents']\n"
     ]
    }
   ],
   "source": [
    "#Claases in a list\n",
    "\n",
    "root='/Users/vasaini/MLHACK/orig'\n",
    "dirlist = [ item for item in os.listdir(root) if os.path.isdir(os.path.join(root, item)) ]\n",
    "print (dirlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_test_split' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-9b2434c22d06>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# Split our data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m train, test, train_labels, test_labels = train_test_split(features,\n\u001b[0m\u001b[0;32m      4\u001b[0m                                                           \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                                                           \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.33\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'train_test_split' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "# Split our data\n",
    "train, test, train_labels, test_labels = train_test_split(features,\n",
    "                                                          labels,\n",
    "                                                          test_size=0.33,\n",
    "                                                          random_state=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classification SVM-Multi-class\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy \n",
    "from PIL import Image\n",
    "import os\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "for file in os.listdir('C:/Users/vasaini/MLHACK/resized'):\n",
    "    img = Image.open(os.path.join('Users/vasaini/Desktop/Gear images',file))\n",
    "    data = np.array(img)\n",
    "# fit a SVM model to the data\n",
    "model = SVC()\n",
    "model.fit(dataset.data, dataset.target)\n",
    "print(model)\n",
    "# make predictions\n",
    "predicted = model.predict(dataset.data)\n",
    "# Evaluate accuracy\n",
    "print(accuracy_score(test_labels, predicted))\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a confusion matrix\n",
    "knn_predictions = knn.predict(X_test) \n",
    "cm = confusion_matrix(y_test, knn_predictions)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}