{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import gradio as gr\n",
    "import numpy as np\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imagepath = './images/simple_flowers.jpg'\n",
    "# imagepath = './images/rocky_coast.jpg'\n",
    "# imagepath = './images/purple_flower.jpg'\n",
    "imagepath = './images/simple_dots.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = cv2.imread(imagepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(640, 640, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12568ed70>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(640, 640, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(640, 640, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1257fc550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "640"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(image)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resize_image(image):\n",
    "    x = np.shape(image)[0]\n",
    "    y = np.shape(image)[1]\n",
    "    z = np.shape(image)[2]\n",
    "\n",
    "    if (x <= 800) and (y <= 800):\n",
    "        return image\n",
    "\n",
    "    new_x = x\n",
    "    new_y = y\n",
    "    divider_x = x//4\n",
    "    divider_y = y//4\n",
    "\n",
    "    while (new_x > 800) or (new_y > 800):\n",
    "        new_x = new_x//divider_x\n",
    "        new_y = new_y//divider_y\n",
    "\n",
    "    res = cv2.resize(image, dsize=(x//2, y//2), interpolation=cv2.INTER_LINEAR)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1258707f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(640, 640, 3)\n",
      "1280 1280\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(640, 640, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.shape(image))\n",
    "x = np.shape(image)[0]\n",
    "y = np.shape(image)[1]\n",
    "print(x*2, y*2)\n",
    "test_res = cv2.resize(image, dsize=(x*3, y*3), interpolation=cv2.INTER_LINEAR)\n",
    "np.shape(test_res)\n",
    "res = resize_image(image)\n",
    "np.shape(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1258bb580>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.shape(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x//2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshaping the image into a 2D array of pixels and 3 color values (RGB)\n",
    "pixel_vals = image.reshape((-1,3))\n",
    "np.shape(pixel_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to float type\n",
    "pixel_vals = np.float32(pixel_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.shape(pixel_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the below line of code defines the criteria for the algorithm to stop running, \n",
    "#which will happen is 100 iterations are run or the epsilon (which is the required accuracy) \n",
    "#becomes 85%\n",
    "criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.85)\n",
    " \n",
    "# then perform k-means clustering with number of clusters defined as 3\n",
    "#also random centres are initially choosed for k-means clustering\n",
    "k = 2\n",
    "retval, labels, centers = cv2.kmeans(pixel_vals, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)\n",
    "\n",
    "# convert data into 8-bit values\n",
    "centers = np.uint8(centers)\n",
    "segmented_data = centers[labels.flatten()]\n",
    " \n",
    "# reshape data into the original image dimensions\n",
    "segmented_image = segmented_data.reshape((image.shape))\n",
    " \n",
    "plt.imshow(segmented_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import cv2\n",
    "im = cv2.imread(imagepath)\n",
    "img = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
    "r, g, b = cv2.split(img)\n",
    "r = r.flatten()\n",
    "g = g.flatten()\n",
    "b = b.flatten()#plotting \n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "ax.scatter(r, g, b, c=labels)\n",
    "ax.scatter(centers[:,0], centers[:,1], centers[:,2], s=50, c='r', marker=\"o\", label='cluster centers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig"
   ]
  }
 ],
 "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",
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