datasetgenerator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.misc\n",
    "import skimage.io\n",
    "import os\n",
    "import cv2\n",
    "\n",
    "path = r\"C:\\Users\\Connor\\Downloads\\eoinf96-7-segmentdigitgenerator-e16f11b642a7\\images\"\n",
    "\n",
    "video = skimage.io.imread_collection(os.path.join(path, '*.png'), conserve_memory=True, plugin=None)\n",
    "\n",
    "features = np.zeros((len(video), 28*28 + 1), dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "labels = {f\"{i}\": i for i in range(10)}\n",
    "\n",
    "for i, filename in enumerate(video.files):\n",
    "    digit = os.path.basename(filename)[0]\n",
    "    label = labels[digit]\n",
    "    feature = video[i]\n",
    "    feature = cv2.resize(feature, (28, 28))\n",
    "    features[i, :-1] = feature.flatten()\n",
    "    features[i, -1] = label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "np.random.shuffle(features)\n",
    "np.save(\"sevensegdataset\", features)\n",
    "\n",
    "## Want to do some future work?\n",
    "## Download my dataset from https://www.reddit.com/r/MachineLearning/comments/j10ub1/p_like_mnist_but_for_7segment_displays/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19ec2940520>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(features[696, :-1].reshape((28, 28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "X = features[:, :-1]\n",
    "Y = features[:, -1]\n",
    "clf = RandomForestClassifier(n_estimators=10)\n",
    "clf = clf.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['estimator.joblib']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(clf, 'estimator.joblib')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

readscale.ipynb