OpenCV_Practice/chap3_image_processing/chap3_image_processing.ipynb

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   "source": [
    "## 第三章：使用opencv3处理图像"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 滤波"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "from scipy import ndimage\n",
    "\n",
    "kernel_3x3 = np.array([[-1, -1, -1],\n",
    "                     [-1, 8, -1],\n",
    "                     [-1, -1, -1]])\n",
    "\n",
    "kernel_5x5 = np.array([[-1, -1, -1, -1, -1],\n",
    "                      [-1, 1, 2, 1, -1],\n",
    "                      [-1, 2, 4, 2, -1],\n",
    "                      [-1, 1, 2, 1, -1],\n",
    "                      [-1, -1, -1, -1, -1]])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv.imread('MyPic.png', 0) #gray\n",
    "\n",
    "k3 = ndimage.convolve(img, kernel_3x3)\n",
    "k5 = ndimage.convolve(img, kernel_5x5)\n",
    "\n",
    "blurred = cv.GaussianBlur(img, (11,11), 0)\n",
    "g_hpf = img - blurred\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.imshow('origin', cv.imread('MyPic.png'))\n",
    "cv.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cv.imshow('3x3', k3)\n",
    "cv.imshow('5x5', k5)\n",
    "cv.imshow('g_hpf', g_hpf)\n",
    "cv.waitKey(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Canny边缘检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canny_img = cv.Canny(img, 200, 300)\n",
    "cv.imwrite('canny.jpg', canny_img)\n",
    "cv.imshow('canny', canny_img)\n",
    "cv.waitKey(0)"
   ]
  }
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