Tutorial 2-without solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exploring Various color spaces \n",
    "#GRAYSCALE\n",
    "#HSV - Hue Saturation Value\n",
    "#LAB - Luminosity A(green-red) B(yellow-Blue)\n",
    "#threshold selection and canny edge detection alogrithm\n",
    "#drawing contours and counting them\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "image = cv2.imread(\"puppy.jpg\")\n",
    "(height,width,depth) = image.shape\n",
    "\n",
    "cv2.namedWindow('Image', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"Image\", image)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# convert the image to grayscale\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) \n",
    "cv2.namedWindow('Gray', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"Gray\", gray)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "\n",
    "#converting to HSV colorspace\n",
    "hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) \n",
    "cv2.namedWindow('hsv', cv2.WINDOW_NORMAL)\n",
    "#display converted HSV image\n",
    "cv2.imshow(\"hsv\", hsv)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "#converting HSV image to Grayscale\n",
    "hsv_gray=cv2.cvtColor(hsv, cv2.COLOR_BGR2GRAY)\n",
    "cv2.namedWindow('hsv_gray', cv2.WINDOW_NORMAL)\n",
    "#display converted HSV gray image\n",
    "cv2.imshow('hsv_gray', hsv_gray)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting into high contrast image\n",
    "import cv2\n",
    "\n",
    "#-----Reading the image-----------------------------------------------------\n",
    "img = cv2.imread('puppy.jpg', 1)\n",
    "cv2.imshow(\"img\",img) \n",
    "cv2.waitKey(0)\n",
    "#-----Converting image to LAB Color model----------------------------------- \n",
    "lab= cv2.cvtColor(img, cv2.COLOR_BGR2LAB)\n",
    "cv2.imshow(\"lab\",lab)\n",
    "cv2.waitKey(0)\n",
    "#-----Splitting the LAB image to different channels-------------------------\n",
    "l, a, b = cv2.split(lab)\n",
    "cv2.imshow('l_channel', l)\n",
    "cv2.imshow('a_channel', a)\n",
    "cv2.imshow('b_channel', b)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "#-----Applying CLAHE to L-channel-------------------------------------------\n",
    "clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))\n",
    "cl = clahe.apply(l)\n",
    "cv2.imshow('CLAHE output', cl)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "#-----Merge the CLAHE enhanced L-channel with the a and b channel-----------\n",
    "limg = cv2.merge((cl,a,b))\n",
    "cv2.imshow('limg', limg)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "#-----Converting image from LAB Color model to RGB model--------------------\n",
    "high_contrast = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imshow(\"high_contrast\", high_contrast)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_gray = cv2.cvtColor(high_contrast, cv2.COLOR_BGR2GRAY) \n",
    "\n",
    "cv2.namedWindow('High Contrast Gray', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"High Contrast Gray\", high_gray)\n",
    "cv2.waitKey(0)\n",
    "#writing to file\n",
    "cv2.destroyAllWindows() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying gaussian blur on gray\n",
    "# apply a Gaussian blur with a 11x11 kernel to the image to smooth it,\n",
    "# useful when reducing high frequency noise\n",
    "blurred_gray = cv2.GaussianBlur(gray, (13,13), 0)\n",
    "cv2.imshow(\"Blurred Gray\", blurred_gray)\n",
    "\n",
    "H_blurred_gray = cv2.GaussianBlur(high_gray, (15,15), 0)\n",
    "cv2.imshow(\"High contrast Blurred Gray\", H_blurred_gray)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The Canny edge detector is an edge detection operator \n",
    "#that uses a multi-stage algorithm to detect a wide range of edges in images.\n",
    "#It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection\n",
    "#explaining why the technique works. \n",
    "\n",
    "#Canny algorithm works best on gray scale. It takes four inputs:\n",
    "#Grayscale image\n",
    "#min threshold\n",
    "#max threshold\n",
    "#aperture size where default is 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "edged = cv2.Canny(hsv_gray, 40, 70, 3)\n",
    "cv2.namedWindow('Edged', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"Edged\", edged)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "edged_2 = cv2.Canny(blurred_gray, 10, 70, 3)\n",
    "cv2.namedWindow('Edged', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"Edged\", edged)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#threshold the image by setting all pixel values less than 225 to 255 (white; foreground) and all pixel values >= 170 to 255\n",
    "#(black; background), thereby segmenting the image\n",
    "thresh = cv2.threshold(blurred_gray, 70,255, cv2.THRESH_BINARY_INV)[1]\n",
    "cv2.namedWindow('Thresh', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"Thresh\", thresh)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "lower_threshold=80\n",
    "# try various thresholds\n",
    "#cv2.THRESH_BINARY\n",
    "#cv2.THRESH_BINARY_INV\n",
    "#cv2.THRESH_TRUNC\n",
    "#cv2.THRESH_TOZERO\n",
    "#cv2.THRESH_TOZERO_INV\n",
    "ret,thresh1 = cv2.threshold(blurred_gray,80,255,cv2.THRESH_BINARY)\n",
    "ret,thresh2 = cv2.threshold(blurred_gray,80,255,cv2.THRESH_BINARY_INV)\n",
    "ret,thresh3 = cv2.threshold(blurred_gray,80,255,cv2.THRESH_TRUNC)\n",
    "ret,thresh4 = cv2.threshold(blurred_gray,80,255,cv2.THRESH_TOZERO)\n",
    "ret,thresh5 = cv2.threshold(blurred_gray,80,255,cv2.THRESH_TOZERO_INV)\n",
    "\n",
    "titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']\n",
    "images = [blurred_gray, thresh1, thresh2, thresh3, thresh4, thresh5]\n",
    "from matplotlib import pyplot as plt\n",
    "for i in range(0,6):\n",
    "    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')\n",
    "    plt.title(titles[i])\n",
    "    plt.xticks([]),plt.yticks([])\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find contours (i.e., outlines) of the foreground objects in the\n",
    "# thresholded image\n",
    "import imutils\n",
    "cnts = cv2.findContours(edged_2.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "cnts = imutils.grab_contours(cnts )\n",
    "output = image.copy()\n",
    " \n",
    "#loop over the contours\n",
    "for c in cnts:\n",
    "    # draw each contour on the output image with a 3px thick purple\n",
    "    # outline, then display the output contours one at a time\n",
    "    cv2.drawContours(output, [c], -1, (255, 0, 155), 3)\n",
    "    cv2.imshow(\"Contours\", output)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# draw the total number of contours found in purple\n",
    "text = \"I found {} objects!\".format(len(cnts))\n",
    "cv2.putText(output, text, (10, 25),  cv2.FONT_HERSHEY_SIMPLEX, 0.7,(0, 240, 159), 2)\n",
    "cv2.imshow(\"Contours\", output)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "710 598 77 136\n",
      "652 487 1 4\n",
      "588 465 31 61\n",
      "363 448 5 13\n",
      "460 438 116 63\n",
      "618 436 33 48\n",
      "653 411 13 19\n",
      "725 356 52 110\n",
      "528 344 24 27\n",
      "475 344 16 22\n",
      "234 334 14 26\n",
      "232 297 20 22\n",
      "229 272 29 22\n",
      "778 260 49 132\n",
      "664 255 6 8\n",
      "778 247 4 6\n",
      "418 234 23 26\n",
      "605 223 48 51\n",
      "404 223 28 15\n",
      "583 178 184 482\n",
      "315 142 79 332\n",
      "Lower X: 229 Lower Y: 142 Higher X: 787 Higher Y: 734\n"
     ]
    }
   ],
   "source": [
    "# find contours (i.e., outlines) of the foreground objects in the\n",
    "# thresholded image\n",
    "\n",
    "contours,hierarchy = cv2.findContours(thresh.copy(),  cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "temp_x_lower=int(width/2)\n",
    "temp_y_lower=int(height/2)\n",
    "temp_x_higher=int(width/2)\n",
    "temp_y_higher=int(height/2)\n",
    "for cnt in contours:\n",
    "    x,y,w,h = cv2.boundingRect(cnt)\n",
    "    print(x,y,w,h)\n",
    "    \n",
    "    if temp_x_higher+w  < (x+w):\n",
    "        temp_x_higher =int(x+w)\n",
    "    if temp_y_higher+h  < (y+h):\n",
    "        temp_y_higher= int(y+h)\n",
    "        \n",
    "    if temp_x_lower > int(x):\n",
    "        temp_x_lower=int(x)\n",
    "    if temp_y_lower > int(y): \n",
    "        temp_y_lower=int(y)\n",
    "\n",
    "print(\"Lower X:\",temp_x_lower,\"Lower Y:\",temp_y_lower,\"Higher X:\", temp_x_higher,\"Higher Y:\",temp_y_higher) \n",
    "    \n",
    "bb_img = cv2.rectangle(image,(temp_x_lower,temp_y_lower),(int(temp_x_higher),int(temp_y_higher)),(0,255,0),2)\n",
    "cv2.imshow(\"Bounding Box\",bb_img)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exercise 2\n",
    "#change gaussian blur and utilize the various color spaces if you can get the right number of cars in carpark image. \n",
    "#Think of using other threshold if possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "image = cv2.imread(\"carpark3.jpg\")\n",
    "(height,width,depth) = image.shape\n",
    "\n",
    "cv2.namedWindow('Image', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"Image\", image)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# convert the image to grayscale\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) \n",
    "cv2.namedWindow('Gray', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"Gray\", gray)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "\n",
    "#converting to HSV colorspace\n",
    "hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) \n",
    "cv2.namedWindow('hsv', cv2.WINDOW_NORMAL)\n",
    "#display converted HSV image\n",
    "cv2.imshow(\"hsv\", hsv)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "#converting HSV image to Grayscale\n",
    "hsv_gray=cv2.cvtColor(hsv, cv2.COLOR_BGR2GRAY)\n",
    "cv2.namedWindow('hsv_gray', cv2.WINDOW_NORMAL)\n",
    "#display converted HSV gray image\n",
    "cv2.imshow('hsv_gray', hsv_gray)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying gaussian blur on gray\n",
    "# apply a Gaussian blur with a 11x11 kernel to the image to smooth it,\n",
    "# useful when reducing high frequency noise\n",
    "blurred_gray = cv2.GaussianBlur(gray, (13,13), 0)\n",
    "cv2.imshow(\"Blurred Gray\", blurred_gray)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#threshold the image by setting all pixel values less than 225 to 255 (white; foreground) and all pixel values >= 170 to 255\n",
    "#(black; background), thereby segmenting the image\n",
    "temp_image=blurred_gray\n",
    "thresh = cv2.threshold(temp_image, 70,255, cv2.THRESH_BINARY_INV)[1]\n",
    "cv2.namedWindow('Thresh', cv2.WINDOW_NORMAL)\n",
    "cv2.imshow(\"Thresh\", thresh)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "lower_threshold=80\n",
    "higher_threshold=255\n",
    "# try various thresholds\n",
    "#cv2.THRESH_BINARY\n",
    "#cv2.THRESH_BINARY_INV\n",
    "#cv2.THRESH_TRUNC\n",
    "#cv2.THRESH_TOZERO\n",
    "#cv2.THRESH_TOZERO_INV\n",
    "ret,thresh1 = cv2.threshold(temp_image,lower_threshold,higher_threshold,cv2.THRESH_BINARY)\n",
    "ret,thresh2 = cv2.threshold(temp_image,lower_threshold,higher_threshold,cv2.THRESH_BINARY_INV)\n",
    "ret,thresh3 = cv2.threshold(temp_image,lower_threshold,higher_threshold,cv2.THRESH_TRUNC)\n",
    "ret,thresh4 = cv2.threshold(temp_image,lower_threshold,higher_threshold,cv2.THRESH_TOZERO)\n",
    "ret,thresh5 = cv2.threshold(temp_image,lower_threshold,higher_threshold,cv2.THRESH_TOZERO_INV)\n",
    "\n",
    "titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']\n",
    "images = [gray, thresh1, thresh2, thresh3, thresh4, thresh5]\n",
    "from matplotlib import pyplot as plt\n",
    "for i in range(0,6):\n",
    "    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')\n",
    "    plt.title(titles[i])\n",
    "    plt.xticks([]),plt.yticks([])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}