14-Thresholding, Binarization & Adaptive Thresholding.ipynb

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    "<img src=\"http://akhavanpour.ir/notebook/images/srttu.gif\" alt=\"SRTTU\" style=\"width: 150px;\"/>\n",
    "\n",
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   "source": [
    "## Thresholding, Binarization & Adaptive Thresholding\n",
    "\n",
    "\n",
    "In thresholding, we convert a grey scale image to it's binary form"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "در ابتدا با numpy تصویر را باینری می‌کنیم.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Load our image as greyscale \n",
    "image = cv2.imread('./images/gradient.jpg')\n",
    "\n",
    "new_image = np.copy(image)\n",
    "new_image[new_image <  127] = 0\n",
    "new_image[new_image >= 127] = 255\n",
    "\n",
    "cv2.imshow('Original', image)\n",
    "cv2.imshow('After Threshhold, Binary Image', new_image)\n",
    "cv2.waitKey(0) \n",
    "    \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "و خروجی با تصویر در دنیای واقعی</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Load our image as greyscale \n",
    "image = cv2.imread('./images/Lusin-1.jpg', 0)\n",
    "\n",
    "new_image = np.copy(image)\n",
    "new_image[new_image <  127] = 0\n",
    "new_image[new_image >= 127] = 255\n",
    "\n",
    "cv2.imshow('Original', image)\n",
    "cv2.imshow('After Threshhold, Binary Image', new_image)\n",
    "cv2.waitKey(0) \n",
    "    \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "برای اینکار می‌توانیم از تابع threshold در opencv استفاده کنیم\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Load our image as greyscale \n",
    "image = cv2.imread('images/gradient.jpg',0)\n",
    "cv2.imshow('Original', image)\n",
    "\n",
    "# Values below 127 goes to 0 (black, everything above goes to 255 (white)\n",
    "ret,thresh1 = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)\n",
    "cv2.imshow('1 Threshold Binary', thresh1)\n",
    "cv2.waitKey(0) \n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "پارامترهای مختلف تابع threshold</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Load our image as greyscale \n",
    "image = cv2.imread('images/gradient.jpg',0)\n",
    "cv2.imshow('Original', image)\n",
    "\n",
    "# Values below 127 goes to 0 (black, everything above goes to 255 (white)\n",
    "ret,thresh1 = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)\n",
    "cv2.imshow('1 Threshold Binary', thresh1)\n",
    "\n",
    "# Values below 127 go to 255 and values above 127 go to 0 (reverse of above)\n",
    "ret,thresh2 = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "cv2.imshow('2 Threshold Binary Inverse', thresh2)\n",
    "\n",
    "# Values above 127 are truncated (held) at 127 (the 255 argument is unused)\n",
    "ret,thresh3 = cv2.threshold(image, 127, 255, cv2.THRESH_TRUNC)\n",
    "cv2.imshow('3 THRESH TRUNC', thresh3)\n",
    "\n",
    "# Values below 127 go to 0, above 127 are unchanged  \n",
    "ret,thresh4 = cv2.threshold(image, 127, 255, cv2.THRESH_TOZERO)\n",
    "cv2.imshow('4 THRESH TOZERO', thresh4)\n",
    "\n",
    "# Resever of above, below 127 is unchanged, above 127 goes to 0\n",
    "ret,thresh5 = cv2.threshold(image, 127, 255, cv2.THRESH_TOZERO_INV)\n",
    "cv2.imshow('5 THRESH TOZERO INV', thresh5)\n",
    "cv2.waitKey(0) \n",
    "    \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there a better way off thresholding?\n",
    "\n",
    "The biggest downfall of those simple threshold methods is that we need to provide the threshold value (i.e. the 127 value we used previously).\n",
    "#### What if there was a smarter way of doing this?\n",
    "\n",
    "There is with, Adaptive thresholding. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Load our new image\n",
    "image = cv2.imread('images/Origin_of_Species.jpg', 0)\n",
    "\n",
    "cv2.imshow('Original', image)\n",
    "cv2.waitKey(0) \n",
    "\n",
    "# Values below 127 goes to 0 (black, everything above goes to 255 (white)\n",
    "ret,thresh1 = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)\n",
    "cv2.imshow('Threshold Binary', thresh1)\n",
    "cv2.waitKey(0) \n",
    "\n",
    "\n",
    "\n",
    "# Using adaptiveThreshold\n",
    "thresh = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \n",
    "                               cv2.THRESH_BINARY, 3, 5) \n",
    "cv2.imshow(\"Adaptive Mean Thresholding\", thresh) \n",
    "cv2.waitKey(0) \n",
    "\n",
    "# Using adaptiveThreshold + GaussianBlur\n",
    "blur = cv2.GaussianBlur(image,(5,5),0)\n",
    "thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \n",
    "                               cv2.THRESH_BINARY, 3, 5) \n",
    "cv2.imshow(\"Adaptive Mean Thresholding with GaussianBlur\", thresh) \n",
    "cv2.waitKey(0) \n",
    "\n",
    "\n",
    "ret2,th2 = cv2.threshold(image,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "cv2.imshow(\"Otsu's Thresholding\", th2) \n",
    "cv2.waitKey(0) \n",
    "\n",
    "# Otsu's thresholding after Gaussian filtering\n",
    "blur = cv2.GaussianBlur(image,(5,5),0)\n",
    "ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "cv2.imshow(\"Guassian Otsu's Thresholding\", th3) \n",
    "cv2.waitKey(0) \n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.opencv.org/3.4.0/d7/d4d/tutorial_py_thresholding.html"
   ]
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   "cell_type": "markdown",
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   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> دانشگاه تربیت دبیر شهید رجایی<br>مباحث ویژه - آشنایی با بینایی کامپیوتر<br>علیرضا اخوان پور<br>96-97<br>\n",
    "</div>\n",
    "<a href=\"https://www.srttu.edu/\">SRTTU.edu</a> - <a href=\"http://class.vision\">Class.Vision</a> - <a href=\"http://AkhavanPour.ir\">AkhavanPour.ir</a>\n",
    "</div>"
   ]
  },
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