Image Compression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.io import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = imread('college.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x85e5590>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8628df0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = img/225"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        ...,\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333]],\n",
       "\n",
       "       [[1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        ...,\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333]],\n",
       "\n",
       "       [[1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        ...,\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        ...,\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333]],\n",
       "\n",
       "       [[1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        ...,\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333]],\n",
       "\n",
       "       [[1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        ...,\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "        [1.13333333, 1.13333333, 1.13333333, 1.13333333]]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 1600, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_format = img.reshape(900 * 1600, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "       [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "       [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "       ...,\n",
       "       [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "       [1.13333333, 1.13333333, 1.13333333, 1.13333333],\n",
       "       [1.13333333, 1.13333333, 1.13333333, 1.13333333]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(img_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = kmeans.predict(img_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.13309926, 1.13313717, 1.13308468, 1.13333333],\n",
       "       [0.03460473, 0.39241423, 0.19658565, 1.13333333],\n",
       "       [0.773726  , 0.69653425, 0.46204284, 1.13333333]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.13309926, 1.13313717, 1.13308468, 1.13333333],\n",
       "       [1.13309926, 1.13313717, 1.13308468, 1.13333333]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_[[0,0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_new = kmeans.cluster_centers_[clusters]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img = img_new.reshape(900, 1600,4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa72d2b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5d39f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(new_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}