cv2学习笔记2（形态学处理）.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cv2学习笔记2\n",
    "## 形态学处理\n",
    "- 形态学处理的核心就是定义结构元素getStructuringElement\n",
    "### 定义结构元素cv2.getStructuringElement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gK1eutMvDz2NL8rnWXFhFxfWEMYfDIStWrJAPf/jDtoBYIpnNZhckOCIijzzy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\nKoqiVAEVV0VRlCqg4qooilIF/n9waetn0bYXMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108bcf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 0 0]\n",
      " [0 0 1 0 0]\n",
      " [1 1 1 1 1]\n",
      " [0 0 1 0 0]\n",
      " [0 0 1 0 0]]\n",
      "[[0 0 1 0 0]\n",
      " [1 1 1 1 1]\n",
      " [1 1 1 1 1]\n",
      " [1 1 1 1 1]\n",
      " [0 0 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "import cv2      \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "img = cv2.imread('C:/Users/situ.st.1/Pictures/hand.jpg') \n",
    " \n",
    "plt.imshow(img,\"gray\")\n",
    "plt.show()\n",
    "np.set_printoptions(threshold='nan')\n",
    "element = cv2.getStructuringElement(cv2.MORPH_CROSS,(5,5)) #十字形\n",
    "print element\n",
    "element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)) #椭圆\n",
    "print element\n",
    "#定义椭圆（MORPH_ELLIPSE）和十字形（MORPH_CROSS）长方形 (MORPH_RECT)结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 腐蚀cv2.erode\n",
    "- 第一个参数是需要处理的图像，第二个是结构元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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819q1a43f7zfBYNB4vd5O79XQ0GAaGhrMxx9/3Om95syZY1paWozH4zEej8f4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\nXJVSKg00uCqlVBr8f4M85TU1b+ToAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd251080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(5, 5))  \n",
    "kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5, 5))    \n",
    "eroded = cv2.erode(img,kernel) \n",
    " \n",
    "plt.imshow(eroded,\"gray\")\n",
    "plt.show()\n",
    "#cv2.imshow(\"Eroded Image\",eroded);\n",
    "#cv2.waitKey(0)  \n",
    "#cv2.destroyAllWindows()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 膨胀cv2.dilate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9/f1ZDzf96KOP7FrmXXfdxZYtW9I63vrjYr3IEpGsatKqeEypt0Dykf4kEakC\nnhaR48Z8btJtNzXG3A/cD9rmWiys7lCpb6yfffbZnF5jKo+7Xq+X4eFhe0b+b37zmzm59rJly+xg\n6vf7cTqd9j1nqqmpCY/HY58n3dmsrD8yLpeLaDRKPB5naGgoqzKp4pBW45Uxpgd4GTgf2C8iDQDJ\nn1Ze5RZgbsphjcltqogFg0Hcbrc9Equvr48PPvggZ+c/8cQT2bdvH5DIWjBRELKCS39/f85niEod\nmOByuXLSp3RoaIh3330XSD+4pjrmmGMoLy/Xmus0MWlwFZHaZI0VEfED5wDvA+sAaxbaqwGrirMO\nWCUiXhFZCDQBG3NdcJU/ZWVlLFy4MGfnW758uZ2hoKOjY9IpA40xBAIBZs+endO3+VZHfYfDYdcY\ns+3idfnll9vzHrz77rtcddVV7Nixg+7u7lH7TaU9dffu3QBac50mplJzbQBeFpGtwJsk2lz/APwE\nOEdEPgT+v+TvGGO2AWuB7cDzwGrtKVD8QqGQPduV3+/njTfeyPqc4XCYU089lerqaubMOajZfVy1\ntbUAnHfeeaxduzbrcqRKDVy33347Z5xxRsbnWrFixUHdpuLxOOecc85hnfFeFadJ21yNMVuB5YfY\n3gn8j3GO+V/A/8q6dOqwcTgc9uCArVu3Zn2+WCzG9ddfT3d3t11jTOdY68VVLBbLSa6jSCSC2+0m\nFArhdDrxeDxcf/31GXfreuqpp+jp6bHPZQmFQqN6Wlimch0RwRij6V6mCR2hpUa58sorsz5Hf38/\nDzzwAK+88gpdXV1s27YtrfZbq3b5zDPPcPbZZ2ddHoA777yTjz76iN7eXjuAZTNd4Ve+8hUgEfyt\nx3lLMBjkkUceyfjc2faIUEXCmqezkAuJfrO6FHBxu92mtrbWtLe3m2zs3bvXXH755WbevHlm1qxZ\nGZXF4/GY2tpaU1tba84+++ysymM56qij7PP7/X5TW1ub1flEZNRinbuystLU1NSY2bNnp33OlStX\nmmAwaHw+X8G/D7pggE0mi7imNVcFJIa4hsNhu70zU52dnbzxxhtZpdg2yUm5nU5n1h3zLXv3ftr1\nOhaL8Ytf/CIn5x3L4/HgcrkyHhBgTe2oSp8GV5UzLS0tXHfddUDizXy6ba2W8vJy4vH4qOwD2Tj+\n+ONH9d3Ntm/rZMrKyuwhvenYt2+fTpQ9jeiUgwoY3f8zE5FIhGOOOYaBgQE8Hg9OpzPj4BoKhair\nq+NLX/pUe98pAAASg0lEQVRSVmWydHV1EQgE7G5Xvb29Wd9vKpPStayystLOJhCJRNJK2TJz5kz6\n+vooKyub9rOAHQm05qpGsTKspiMcDrNgwYJRv2caWC0DAwP8+Mc/zuocAOeeey4w+o9HVVUVkPlY\n/mAwOPadgc16SSYiGdW8rQltVOnT4KqARDCLx+N89rOfTbsT+/HHH095eXlOukxB4rHa7XbnpFmg\np6cHY8yoOQTGZjZIV1dX17ifWaPQlixZkvZ5n3jiiYzLpIqPBlcFMKoWtmLF1KawjEQizJo1y/49\n0xqXNbWgtQwNDdHQ0JD1rPy9vb20tbXZgyPGynbqwvF0dHSwceNGLrzwwrT+UFkBeWxtWJUmDa4K\n+HQu0d27d9PS0sLKlSsnTJ3d0dFBQ0MDQ0ND7Nu3jw8//PCg2mCmQcJqp8w2xfQJJ5wAcFCn/Fy2\nt44VjUYZHBykp6eHDRs2cOKJJ6Y185aVulyVPv1XVLbu7m6Ghobo6+tj586dLF++nFNOOcUeK//q\nq68SiUR49dVX+e53v4vb7WZoaGhU++p4bZHpiEajdmDMRFtbm137jkQiB725N8Zw/vnnZ3z+Z555\nZtzPrD8wDoeDeDxOOBzme9/73pTPPTAwkFU3NlU8tLeAGlc8HufAgQOce+651NTU2NuznQN1Kh58\n8MGMj7UCqzWj1sjISE5n9r/lllvG/UxE7Jqny+Wit7eXCy64YMrnzqQLlypOGlzVhPr6+g6aAi8W\ni9HR0ZHT6QBTr3HUUUdl9Xic2gxgrbtcLnvO1K997WsYYzIa/nrWWWdN2KPCmmfA4XDgdDrT7n1R\nWVlJNBqd1plujxQaXNUhdXZ22m2Te/bsob29fdTjqsvlOii4ZtoU4Pf78Xg8xONxFixYwKZNmSen\n6O7uxufz2bNVWSOerKDn8XhYtmxZxvMK7N+/n2AwOGkSQRFJu0dCT08PHo9HX2hNExpc1bhSJ34e\n2w6Yy0dXa1RSY2Nj1ue99NJL7aBmtQVbPQWcTicOhyOraxyq10GqcDic8Sgrq/+tJiecHvSFlio4\nYwyzZ8/OybmsGnZqTwOv14vT6cxJWpfJZDPTliXfZVSHx1QyEfhEZKOIbBGRbSLyP5Pb7xSRFhHZ\nnFwuTDlmjYg0i8gOETkvnzegSl9qTc3KDpuplpYWIpHIIWunFRUVXHrppVxzzTUZnfvGG2+ks7Nz\nwi5qbrfb7i3hdrtZvvygqZAntHnzZqqrqw/q+6tKz1SaBULA2caYARFxA6+JyB+Tn91rjLkndWcR\nWQasAo4FZgMbRGSJ0WwEahwjIyN0dnZmPSLL5XLZNdTU7mGDg4P4/X5aWlqyas+0JrOeaGjvyMgI\nPp8Pl8tFKBSaMBAfit/v55NPPsm4jKp4TCUTgQGsxjd3cpnoG3oJ8JgxJgR8LCLNwEog+7whalpy\nOByEw+FR0wJmIhaLjduDIdu5DgBefPFFotEo5eXlE/ZFTf0skwEL+kJrephSm6uIOEVkM4kMr+uN\nMX9OfvQtEdkqIg+KiJU0aA6Q+n/JvuQ2pWypj7zxeDyrsf5AzrPEHsqBAwfSnkoxk+yyOu3g9DCl\n4GqMiRljTiKRJnuliBwH3AcsAk4CWoF/TufCInKDiGwSkcz73aiSNLYN0eFwsHjx4qzO+V//9V/j\nfubxeKioqKCioiLjtNWptcl0/hD4fD6+/OUvp3Wtn/3sZ2ntr4pTWr0FjDE9wMvA+caY/cmgGwd+\nSeLRH6AFmJtyWGNy29hz3W+MWWGMmdosIWpae+2117I6/sCBA+N+JiKEw2HuvPNOfvjDH6Z97ldf\nfZWZM2cSjUbTHnwQDofTfiGV6R8AVVym0lugVkSqkut+4BzgfRFpSNntMuDd5Po6YJWIeEVkIdAE\nbMxtsdV00tzcnNXxe/fu5dZbbx3384aGBhoaGvj617+e0fm3bdtmv2yLRqNEIpEpHztZ+6yavqZS\nc20AXhaRrcCbJNpc/wD8k4i8k9z+ReDvAIwx24C1wHbgeWC19hRQk8lmBqyenh66u7sn3Gf79u0Z\nn//aa68FEpPApFsLjUajk5ZtrNTBG6qEZZPdMFcLhc/yqMthXMZmTn311VenkBv10EKhkDnmmGNM\neXn5uNc7+uijMz6/xe/3G4/HYzwej3E6nVO+11mzZplFixalda2lS5eOm11Wl8O6aPZXVVrGfgkv\nuuiijM9l9RIIBoPj7nPgwIGM3tqnCgQChMNhwuFw2j0T0q2V79q1K639VXHS4KoKbuHChRkfu2LF\nikkzxVZXV3PnnXdmfI17770342NNMk24lRV3MtFolPr6+oyvp4qHTtyiCsbv9zM8PDzh5NMTCYfD\nRKPRKbWDZpJ40fLhhx9mPJlKb28vsVjMnpVrMi6XK6uyquKhwVUddj6fj/nz5wOJmlqmk7Y0NjYC\niZppa2vruPt98sknWc00tX79eurq6jLKEWb1LLBmvJqKbAdUqOKgwVUddi6Xi5aWFurq6li/fj0+\nny+j84yMjDA8PDxhNlaA+vr6gg0pdbvdBAKBtI4Jh8M6BHYa0DZXVTBjEwema3Bw0E6TPdFLpng8\nzpNPPpnRNRYsWEAkEpk0gE90bYfDwW9/+1vefvvtKR0z2ZyxqjRocFWHXTgcxul0EovFssp06nA4\n7DfxY9tdrdqw2+2mtrY242v09/dnnDI8tVzGGC6//PJJsyx873vf036u04QGV3XYzZw5k7KyMior\nK7OaXs+aVHpsVycrsPp8PpxOJ1/96lfTPvfg4CA1NTV2d7F0BwKkam1tJRQKMTw8zOrVq3njjUNP\nEDcyMsIzzzyTVvusKl4aXFXBZFMjhMQYfGtmLafTaW+32iuNMQQCAf7u7/4u7XOXlZXZubccDseE\n/WgnEolEiEQi7N+/n/3797N161Yuvvhiu2/v0NAQkAisf//3f09/f7+meZkm9IWWOuxaW1sJBoN4\nvV6+/OUvpz2htCUSieByuex2TSv7QDwetx/HM31Ztm3btoyOm4xV237zzTepqamxm0UcDgc+nw+P\nxzNp8kNVGrTmqg47v99vdzcSkYxqsEuWLBm3P6h17mzmKzj22GNxOBw4HA5isZhdw8ylkZERIpEI\nxpjDMh+tOry05qoOu1AohMvlYmBggHg8zooViVknd+zYMeVzjM06MN5MVdn0GfX5fIyMjOT0Mb2n\npwe3223/bt1D6v1oN6zpQYOrOuxisRh9fX0HbT/22GMnfRzftGkTV155JTBxVy4RIRaLZZVGOxqN\n4nK5EJGcTRtotcGq6U+bBVTRiEajfOELXwASc7Tu3buXcDjM2rVrWbJkCUuWLOGyyy4DJh7Oaj6d\nbS3jQNbY2Eg8Hmd4eBifz0d1dfXkBymVQmuuquB27dqFy+Wiurqa5uZmGhsbCYVCox6frUR/4XCY\noaGhCR+dHQ6HnTFgZGSEpUuX8v7770+5PFY/02wGOZSVldnBOVVlZeUha+1q+tHgqorC2Bc6g4OD\neL1eQqEQ0WjUbju1Al86bant7e3MmTNnSr0SotEod911l10eq314qv1cfT6f3UMhNdGgVYMeGRnR\nAHukmOrEr4AT+G/gD8nfg8B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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7e4d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dilated = cv2.dilate(img,kernel)  \n",
    "\n",
    "plt.imshow(dilated,\"gray\")\n",
    "plt.show()\n",
    "#cv2.imshow(\"Dilated Image\",dilated);   \n",
    "#cv2.imshow(\"Origin\", img)  \n",
    "#cv2.waitKey(0)  \n",
    "#cv2.destroyAllWindows()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 开运算和闭运算cv2.morphologyEx\n",
    "- 开运算和闭运算就是将腐蚀和膨胀按照一定的次序进行处理。但这两者并不是可逆的，即先开后闭并不能得到原先的图像。\n",
    "- 闭运算用来连接被误分为许多小块的对象，而开运算用于移除由图像噪音形成的斑点。\n",
    "- 连续使用闭运算和开运算，将获得图像中的主要对象。\n",
    "- 先用开运算后用闭运算，将消除图像中的噪声（即图像中的“小点”），不过这样也会消除一些破碎的对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gK1eutMvDz2NL8rnWXFhFxfWEMYfDIStWrJAPf/jDtoBYIpnNZhckOCIijzzy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\nF9xXpT5RcVVswuEwExMTdHd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x599ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xed73cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2      \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "img = cv2.imread('C:/Users/situ.st.1/Pictures/hand.jpg') \n",
    "plt.imshow(img,\"gray\")\n",
    "plt.show()\n",
    "#定义结构元素  \n",
    "kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(7, 7))  \n",
    "#闭运算  （先膨胀再腐蚀，修补主体小洞）\n",
    "closed = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)    \n",
    "#开运算  （先腐蚀后膨胀，去除噪点） \n",
    "opened = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)  \n",
    "#先闭后开（将获得图像中的主要对象）\n",
    "close_open = cv2.morphologyEx(closed,cv2.MORPH_OPEN,kernel)         \n",
    "#先开后闭（将消除图像中的噪声）\n",
    "open_close = cv2.morphologyEx(opened,cv2.MORPH_OPEN,kernel)\n",
    "\n",
    "plt.figure(figsize=(74,42)) \n",
    "plt.subplot(221),plt.imshow(closed,\"gray\")\n",
    "plt.subplot(222),plt.imshow(opened,\"gray\")\n",
    "plt.subplot(223),plt.imshow(close_open,\"gray\")\n",
    "plt.subplot(224),plt.imshow(open_close,\"gray\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}