Image processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "from skimage.measure import label, regionprops, regionprops_table\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2082881e760>"
      ]
     },
     "execution_count": 110,
     "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": [
    "original = cv2.imread('C:/Users/2109902/Desktop/Picture1.bmp' )\n",
    "plt.imshow(original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret, thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.5.3) C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pip-req-build-sn_xpupm\\opencv\\modules\\imgproc\\src\\connectedcomponents.cpp:4306: error: (-215:Assertion failed) L.channels() == 1 && I.channels() == 1 in function 'cv::connectedComponents_sub1'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11384/238145442.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnectedComponentsWithStats\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthresh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconnectivity\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCV_32S\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.5.3) C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pip-req-build-sn_xpupm\\opencv\\modules\\imgproc\\src\\connectedcomponents.cpp:4306: error: (-215:Assertion failed) L.channels() == 1 && I.channels() == 1 in function 'cv::connectedComponents_sub1'\n"
     ]
    }
   ],
   "source": [
    "output = cv2.connectedComponentsWithStats(thresh, connectivity, cv2.CV_32S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'output' 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~\\AppData\\Local\\Temp/ipykernel_11384/1607452517.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlabel_5\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'output' is not defined"
     ]
    }
   ],
   "source": [
    "label_5 = output == 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'output' 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~\\AppData\\Local\\Temp/ipykernel_11384/1790972352.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'output' is not defined"
     ]
    }
   ],
   "source": [
    "plt.imshow(output, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.5.3) C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pip-req-build-sn_xpupm\\opencv\\modules\\imgproc\\src\\histogram.cpp:3439: error: (-215:Assertion failed) _src.type() == CV_8UC1 in function 'cv::equalizeHist'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11384/530801184.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Contrast adjusting with histogramm equalization\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mgray_equ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mequalizeHist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moriginal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m223\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Histogram equilization'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgray_correct\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m255\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.5.3) C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pip-req-build-sn_xpupm\\opencv\\modules\\imgproc\\src\\histogram.cpp:3439: error: (-215:Assertion failed) _src.type() == CV_8UC1 in function 'cv::equalizeHist'\n"
     ]
    }
   ],
   "source": [
    "# Contrast adjusting with histogramm equalization\n",
    "gray_equ = cv2.equalizeHist(original)\n",
    "plt.subplot(223)\n",
    "plt.title('Histogram equilization')\n",
    "plt.imshow(gray_correct, cmap=\"gray\", vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c0ef975308>"
      ]
     },
     "execution_count": 25,
     "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": [
    "# Dilatation et erosion\n",
    "kernel = np.ones((15,15), np.uint8)\n",
    "img_dilation = cv.dilate(gray_equ, kernel, iterations=1)\n",
    "#img_erode = cv.erode(img_dilation,kernel, iterations=1)\n",
    "# clean all noise after dilatation and erosion\n",
    "#img_erode = cv.medianBlur(img_erode, 7)\n",
    "plt.subplot(221)\n",
    "plt.title('Dilatation')\n",
    "plt.imshow(img_dilation, cmap=\"gray\", vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "objects number is: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Labeling\n",
    "\n",
    "ret, labels = cv.connectedComponents(img_dilation)\n",
    "label_hue = np.uint8(179 * labels / np.max(labels))\n",
    "blank_ch = 255 * np.ones_like(label_hue)\n",
    "labeled_img = cv.merge([label_hue, blank_ch, blank_ch])\n",
    "labeled_img = cv.cvtColor(labeled_img, cv.COLOR_HSV2BGR)\n",
    "labeled_img[label_hue == 0] = 0\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.title('Objects counted:'+ str(ret-1))\n",
    "plt.imshow(labeled_img)\n",
    "print('objects number is:', ret-1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "original"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Area Calculation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simpler approach, suggested by Mark Setchell, is to simply count the number of nonzero pixels in the thresholded image. It computes the same number as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10695\n"
     ]
    }
   ],
   "source": [
    "#gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "ret, thresh = cv.threshold(gray_im,0,255,cv.THRESH_BINARY)\n",
    "area = cv.countNonZero(thresh)\n",
    "print(area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "plt.imshow(img_dilation, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.draw import ellipse\n",
    "from skimage.transform import rotate\n",
    "import math\n",
    "from skimage.measure import label, regionprops, regionprops_table\n",
    "\n",
    "image = np.zeros((600, 600))\n",
    "\n",
    "rr, cc = ellipse(300, 350, 100, 220)\n",
    "image[rr, cc] = 1\n",
    "\n",
    "image = rotate(image, angle=15, order=0)\n",
    "\n",
    "rr, cc = ellipse(100, 100, 60, 50)\n",
    "image[rr, cc] = 1\n",
    "\n",
    "label_img = label(image)\n",
    "regions = regionprops(label_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.imshow(img_dilation, cmap=plt.cm.gray)\n",
    "\n",
    "for props in regions:\n",
    "    y0, x0 = props.centroid\n",
    "    orientation = props.orientation\n",
    "  \n",
    "    x2 = x0 - math.sin(orientation) * 0.5 * props.major_axis_length\n",
    "    y2 = y0 - math.cos(orientation) * 0.5 * props.major_axis_length\n",
    "\n",
    "    ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)\n",
    "    ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)\n",
    "    ax.plot(x0, y0, '.g', markersize=15)\n",
    "\n",
    "    minr, minc, maxr, maxc = props.bbox\n",
    "    bx = (minc, maxc, maxc, minc, minc)\n",
    "    by = (minr, minr, maxr, maxr, minr)\n",
    "    ax.plot(bx, by, '-b', linewidth=2.5)\n",
    "\n",
    "ax.axis((0, 600, 600, 0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import argparse\n",
    "from sklearn.cluster import KMeans\n",
    "#from sklearn_extra.cluster import KMedoids\n",
    "import numpy as geek \n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from skimage.measure import label, regionprops, regionprops_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'label' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\RASHAD~1\\AppData\\Local\\Temp/ipykernel_13940/779931027.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlabel_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel_3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'label' is not defined"
     ]
    }
   ],
   "source": [
    "label_image = label(label_3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "props = regionprops_table(output, properties=('centroid',\n",
    "                                                 'orientation',\n",
    "                                                'minor_axis_length',\n",
    "                                                 'major_axis_length',\n",
    "                                                 'area',\n",
    "                                                'perimeter'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "measure = pd.DataFrame(props)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>orientation</th>\n",
       "      <th>minor_axis_length</th>\n",
       "      <th>major_axis_length</th>\n",
       "      <th>area</th>\n",
       "      <th>perimeter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.5</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>71.582121</td>\n",
       "      <td>91.214034</td>\n",
       "      <td>4898</td>\n",
       "      <td>278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.785398</td>\n",
       "      <td>85.407699</td>\n",
       "      <td>85.407699</td>\n",
       "      <td>5729</td>\n",
       "      <td>279.764502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   centroid-0  centroid-1  orientation  minor_axis_length  major_axis_length  \\\n",
       "0        40.5        48.0     1.570796          71.582121          91.214034   \n",
       "1       141.0        93.0     0.785398          85.407699          85.407699   \n",
       "\n",
       "   area   perimeter  \n",
       "0  4898  278.000000  \n",
       "1  5729  279.764502  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perform Prewitt, Sobel, Roberts, Laplace, Kirsch, operators(filtering)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sobel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sobel_8u' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\RASHAD~1\\AppData\\Local\\Temp/ipykernel_13940/327893918.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msobel_8u\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'gray'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'sobel_8u' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 288x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4))\n",
    "plt.imshow(sobel_8u, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f6389409c8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "left = 0.125\n",
    "right = 0.7\n",
    "bottom = 0.2\n",
    "top = 0.9\n",
    "wspace = 0.02\n",
    "hspace = 0.0\n",
    "plt.subplots_adjust(left=left, right=right, bottom=bottom, top=top, wspace=wspace, hspace=hspace)\n",
    "\n",
    "# Create Sobel kernel\n",
    "gx_kernel = np.array([[-1,0,1],[-2,0,2],[-1,0,1]])\n",
    "gy_kernel = np.array([[-1,-2,-1],[0,0,0],[1,2,1]])\n",
    "\n",
    "# Apply kernel to image\n",
    "edge_gx = cv2.filter2D(original, -1, gx_kernel)\n",
    "edge_gy = cv2.filter2D(original, -1, gy_kernel)\n",
    "\n",
    "# Using Sobel API in OpenCV\n",
    "sobelx = cv2.Sobel(original, -1, 1, 0, ksize=3)\n",
    "sobely = cv2.Sobel(original, -1, 0, 1, ksize=3)\n",
    "\n",
    "# Print results\n",
    "merged_hardcoded = np.hstack((original, edge_gx+edge_gy))\n",
    "merged_sobel = np.hstack((original, sobelx+sobely))\n",
    "# plt.subplot(211)\n",
    "# plt.imshow(merged_hardcoded)\n",
    "plt.subplot(212)\n",
    "plt.imshow(merged_sobel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prewitt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Prewitt operator is used in image processing, particularly within edge detection algorithms. Technically, it is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function. At each point in the image, the result of the Prewitt operator is either the corresponding gradient vector or the norm of this vector. The Prewitt operator is based on convolving the image with a small, separable, and integer valued filter in horizontal and vertical directions and is therefore relatively inexpensive in terms of computations like Sobel and Kayyal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import ndimage, misc\n",
    "fig = plt.figure()\n",
    "plt.gray()  # show the filtered result in grayscale\n",
    "ax1 = fig.add_subplot(121)  # left side\n",
    "ax2 = fig.add_subplot(122)  # right side\n",
    "result = ndimage.prewitt(original)\n",
    "ax1.imshow(original)\n",
    "ax2.imshow(result)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f638a2cfc8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "left = 0.125\n",
    "right = 0.7\n",
    "bottom = 0.2\n",
    "top = 0.9\n",
    "wspace = 0.02\n",
    "hspace = 0.0\n",
    "plt.subplots_adjust(left=left, right=right, bottom=bottom, top=top, wspace=wspace, hspace=hspace)\n",
    "\n",
    "# Create Prewitt kernel\n",
    "gx_kernel = np.array([[-1,0,1],[-1,0,1],[-1,0,1]])\n",
    "gy_kernel = np.array([[-1,-1,-1],[0,0,0],[1,1,1]])\n",
    "\n",
    "# Apply kernel to image\n",
    "edge_gx = cv2.filter2D(original, -1, gx_kernel)\n",
    "edge_gy = cv2.filter2D(original, -1, gy_kernel)\n",
    "\n",
    "# Print results\n",
    "merged = np.hstack((original, edge_gx+edge_gy))\n",
    "plt.imshow(merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rober filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f6389e7e08>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "left = 0.125\n",
    "right = 0.7\n",
    "bottom = 0.2\n",
    "top = 0.9\n",
    "wspace = 0.02\n",
    "hspace = 0.0\n",
    "#plt.subplots_adjust(left=left, right=right, bottom=bottom, top=top, wspace=wspace, hspace=hspace)\n",
    "\n",
    "# Create Roberts kernel\n",
    "gx_kernel = np.array([[1,0],[0,-1]])\n",
    "gy_kernel = np.array([[0,-1],[-1,0]])\n",
    "\n",
    "# Apply kernel to image\n",
    "edge_gx = cv2.filter2D(original, -1, gx_kernel)\n",
    "edge_gy = cv2.filter2D(original, -1, gy_kernel)\n",
    "\n",
    "# Print results\n",
    "merged = np.hstack((original, edge_gx+edge_gy))\n",
    "plt.imshow(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4))\n",
    "plt.imshow(merged, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scharr filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f638e2a688>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "left = 0.125\n",
    "right = 0.7\n",
    "bottom = 0.2\n",
    "top = 0.9\n",
    "wspace = 0.02\n",
    "hspace = 0.0\n",
    "plt.subplots_adjust(left=left, right=right, bottom=bottom, top=top, wspace=wspace, hspace=hspace)\n",
    "\n",
    "# Create Sobel kernel\n",
    "gx_kernel = np.array([[-3,0,3],[-10,0,10],[-3,0,3]])\n",
    "gy_kernel = np.array([[-3,-10,-3],[0,0,0],[3,10,3]])\n",
    "\n",
    "# Apply kernel to image\n",
    "edge_gx = cv2.filter2D(original, -1, gx_kernel)\n",
    "edge_gy = cv2.filter2D(original, -1, gy_kernel)\n",
    "\n",
    "# Using Scharr API in OpenCV\n",
    "scharrx = cv2.Scharr(original, -1, 1, 0)\n",
    "scharry = cv2.Scharr(original, -1, 0, 1)\n",
    "\n",
    "# Print results\n",
    "# merged_hardcoded = np.hstack((img, edge_gx+edge_gy))\n",
    "merged_scharr = np.hstack((original, scharrx+scharry))\n",
    "# plt.subplot(211)\n",
    "# plt.imshow(merged_hardcoded)\n",
    "plt.subplot(212)\n",
    "plt.imshow(merged_scharr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Laplacian filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f63923aa88>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "left = 0.125\n",
    "right = 0.7\n",
    "bottom = 0.2\n",
    "top = 0.9\n",
    "wspace = 0.02\n",
    "hspace = 0.0\n",
    "plt.subplots_adjust(left=left, right=right, bottom=bottom, top=top, wspace=wspace, hspace=hspace)\n",
    "\n",
    "# Using Laplacian API in OpenCV\n",
    "edge = cv2.Laplacian(original, -1)\n",
    "\n",
    "# Print results\n",
    "merged = np.hstack((original, edge))\n",
    "plt.imshow(merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kirsch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "left = 0.125\n",
    "right = 0.7\n",
    "bottom = 0.2\n",
    "top = 0.9\n",
    "wspace = 0.02\n",
    "hspace = 0.0\n",
    "plt.subplots_adjust(left=left, right=right, bottom=bottom, top=top, wspace=wspace, hspace=hspace)\n",
    "\n",
    "# Create Sobel kernel\n",
    "gx_kernel = np.array([[-3,-3,3],[-10,0,10],[-3,0,3]])\n",
    "gy_kernel = np.array([[-3,-10,-3],[0,0,0],[3,10,3]])\n",
    "\n",
    "# Apply kernel to image\n",
    "edge_gx = cv2.filter2D(original, -1, gx_kernel)\n",
    "edge_gy = cv2.filter2D(original, -1, gy_kernel)\n",
    "\n",
    "# Using Scharr API in OpenCV\n",
    "scharrx = cv2.Scharr(original, -1, 1, 0)\n",
    "scharry = cv2.Scharr(original, -1, 0, 1)\n",
    "\n",
    "# Print results\n",
    "# merged_hardcoded = np.hstack((img, edge_gx+edge_gy))\n",
    "merged_scharr = np.hstack((original, scharrx+scharry))\n",
    "# plt.subplot(211)\n",
    "# plt.imshow(merged_hardcoded)\n",
    "plt.subplot(212)\n",
    "plt.imshow(merged_scharr)"
   ]
  }
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