40-Dominant-colors-by-clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9462f48e-ae08-4079-b44d-cd119fd53161",
   "metadata": {},
   "source": [
    "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">پیدا کردن رنگهای غالب</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a267f88-17c0-46c9-88b2-5a03a8351cb5",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">اعمال الگوریتم خوشه بندی و پیدا کردن مراکز خوشه</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "700fcdee-f04d-4e22-beeb-ccbcba0f8a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2  58  21]\n",
      " [  2 179 245]\n",
      " [ 28  59 183]\n",
      " [ 10 139  77]\n",
      " [129 189 227]]\n",
      "[2 2 2 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "n_clusters = 5\n",
    "\n",
    "#read image\n",
    "img = cv2.imread(\"images/felfel-dolme.jpg\")\n",
    "\n",
    "#reshape to a list of pixels\n",
    "flat_img = img.reshape((-1, 3))\n",
    "\n",
    "#using k-means to cluster pixels\n",
    "kmeans = KMeans(n_clusters = n_clusters)\n",
    "kmeans.fit(flat_img)\n",
    "\n",
    "#the cluster centers are our dominant colors.\n",
    "dominant_colors = np.array(kmeans.cluster_centers_, dtype='uint8')\n",
    "\n",
    "labels = kmeans.labels_\n",
    "\n",
    "print(dominant_colors)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a622025d-aaf9-4528-8af0-27fa170d0a9f",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">اعمال درصد هر خوشه به کل پیکسل ها</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c0726764-ea65-45da-b10b-ac44a1f9594f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.27847398, 0.33723431, 0.21056954, 0.14621711, 0.02750506])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages = np.bincount(labels)/len(flat_img)\n",
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1c52843c-a338-42e5-bdb8-1dd9309df17e",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_and_c = zip(percentages,dominant_colors)\n",
    "p_and_c = sorted(p_and_c,reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b22762-f68e-4bcf-991e-673775131383",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">نمایش رنگهای غالب و درصد متناظر آنها</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "86fdbae5-5449-4724-9150-9fbce119ba0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "block = np.ones((50,50,3),dtype='uint')\n",
    "plt.figure(figsize=(12,8))\n",
    "for i in range(n_clusters):\n",
    "    plt.subplot(1,n_clusters,i+1)\n",
    "    block[:] = p_and_c[i][1][::-1] # we have done this to convert bgr(opencv) to rgb(matplotlib) \n",
    "    plt.imshow(block)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.xlabel(str(round(p_and_c[i][0]*100,2))+'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00edb89d-8735-42cc-b0c6-37a0e6b1d04a",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">نمایش درصد به صورت بهتر</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa0b9893-75c7-4f8b-8e3e-7688e9aafa0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar = np.ones((50,500,3),dtype='uint')\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title('Proportions of colors in the image')\n",
    "start = 0\n",
    "i = 1\n",
    "for p,c in p_and_c:\n",
    "    end = start+int(p*500)\n",
    "    if i==n_clusters:\n",
    "        bar[:,start:] = c[::-1]\n",
    "    else:\n",
    "        bar[:,start:end] = c[::-1]\n",
    "    start = end\n",
    "    i+=1\n",
    "\n",
    "plt.imshow(bar)\n",
    "plt.xticks([])\n",
    "plt.yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7c4fa3-a22a-4d43-9f22-6b14e5d0a624",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">نملیش روی تصویر نهایی</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ebec009f-40ec-4ca3-a748-59b04e002c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = 1000\n",
    "cols = int((img.shape[0]/img.shape[1])*rows)\n",
    "img = cv2.resize(img,dsize=(rows,cols),interpolation=cv2.INTER_LINEAR)\n",
    "\n",
    "copy = img.copy()\n",
    "cv2.rectangle(copy,(rows//2-250,cols//2-90),(rows//2+\n",
    "                                             250,cols//2+110),(255,255,255),-1)\n",
    "\n",
    "final = cv2.addWeighted(img,0.1,copy,0.9,0)\n",
    "cv2.putText(final,'Most Dominant Colors in the Image',(rows//2-230,cols//2-40),cv2.FONT_HERSHEY_DUPLEX,0.8,(0,0,0),1,cv2.LINE_AA)\n",
    "\n",
    "\n",
    "start = rows//2-220\n",
    "for i in range(5):\n",
    "    end = start+70\n",
    "    final[cols//2:cols//2+70,start:end] = p_and_c[i][1]\n",
    "    cv2.putText(final,str(i+1),(start+25,cols//2+45),cv2.FONT_HERSHEY_DUPLEX,1,(255,255,255),1,cv2.LINE_AA)\n",
    "    start = end+20\n",
    "\n",
    "plt.show()\n",
    "\n",
    "cv2.imshow('img',final)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43fef7af-0bf4-4de1-a639-2d0941b20ac0",
   "metadata": {},
   "source": [
    "Thanks to: https://towardsdev.com/how-to-find-the-most-dominant-colors-in-an-image-in-python-using-kmeans-clustering-with-source-527ef3e6775f"
   ]
  }
 ],
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