15_Sobel算子、Scharr算子与Laplacian算子.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sobel算子、Scharr算子与Laplacian算子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Sobel算子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① Sobel算子函数：cv2.Sobel(src, ddepth, dx, dy, ksize)，返回值为Sobel算子处理后的图像。\n",
    "\n",
    " - ddepth：图像的深度\n",
    " - dx 和 dy 分别表示水平和竖直方向\n",
    " - ksize 是 Sobel 算子的大小\n",
    " \n",
    "② 靠近最近点的左右和上下的权重最高，所以为±2。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 圆形处理(例)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 #opencv的缩写为cv2\n",
    "import matplotlib.pyplot as plt # matplotlib库用于绘图展示\n",
    "import numpy as np   # numpy数值计算工具包\n",
    "\n",
    "# 魔法指令，直接展示图，Jupyter notebook特有\n",
    "%matplotlib inline   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pie = cv2.imread('01_Picture/06_pie.png') # 读取图像\n",
    "cv2.imshow('img',pie)\n",
    "cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 梯度就是边界点，左边右边不一样\n",
    "def cv_show(img,name):\n",
    "    cv2.imshow(name,img)\n",
    "    cv2.waitKey()\n",
    "    cv2.destroyAllWindows()\n",
    "    \n",
    "# 白到黑是整数，黑到白是负数了，所有的负数会被截断成 0，所以要取绝对值\n",
    "sobelx = cv2.Sobel(pie,cv2.CV_64F,1,0,ksize=3) # 1,0 表示只算水平方向梯度\n",
    "cv_show(sobelx,'sobelx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sobelx = cv2.Sobel(pie,cv2.CV_64F,1,0,ksize=3)\n",
    "sobelx = cv2.convertScaleAbs(sobelx) # 取负数时，取绝对值\n",
    "cv_show(sobelx,'sobelx')\n",
    "\n",
    "sobely = cv2.Sobel(pie,cv2.CV_64F,0,1,ksize=3) # 1,0 只算 y 方向梯度\n",
    "sobely = cv2.convertScaleAbs(sobely) # 取负数时，取绝对值\n",
    "cv_show(sobely,'sobely')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算 x 和 y 后，再求和\n",
    "sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0) # 0是偏置项\n",
    "cv_show(sobelxy,'sobelxy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不建议直接计算,还有重影\n",
    "sobelxy = cv2.Sobel(pie,cv2.CV_64F,1,1,ksize=3)\n",
    "sobelxy = cv2.convertScaleAbs(sobelxy)\n",
    "cv_show(sobelxy,'sobelxy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 人照处理(例)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('01_Picture/07_Lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "cv_show(img,'img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('01_Picture/07_Lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)\n",
    "sobelx = cv2.convertScaleAbs(sobelx)\n",
    "sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)\n",
    "sobely = cv2.convertScaleAbs(sobely)\n",
    "sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)\n",
    "cv_show(sobelxy,'sobelxy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 整体计算有重影和模糊，不建议整体计算\n",
    "img = cv2.imread('01_Picture/07_Lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "sobelxy = cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3)\n",
    "sobelxy = cv2.convertScaleAbs(sobelxy)\n",
    "cv_show(sobelxy,'sobelxy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Scharr算子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 对结果的差异更敏感一些。"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Laplacian算子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① Laplacian算子用的是二阶导，对噪音点更敏感一些。\n",
    "\n",
    "② 如果中心点是边界，它与周围像素点差异的幅度会较大，Laplacian算子根据此特点可以把边界识别出来。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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hwoXIkxbY5Xie12q1Fy9e3GiHgwcPSiSSuO1wPM+ntGO7As/zLpfL4/FstENVVdVO9gfY4XK5XC5XjLdsnuflcrlcLo/dDpIJwCLcNQFgEZIJwCIkE4BFSCYAi5BMABYhmQAsQjIBWPR/fa6/AE5JhOEAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 各个算子区别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('01_Picture/07_Lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "cv_show(img,'img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不同算子的差异\n",
    "img = cv2.imread('01_Picture/07_Lena.jpg',cv2.IMREAD_GRAYSCALE)\n",
    "sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)\n",
    "sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)\n",
    "sobelx = cv2.convertScaleAbs(sobelx)\n",
    "sobely = cv2.convertScaleAbs(sobely)\n",
    "sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)   \n",
    "\n",
    "scharrx = cv2.Scharr(img,cv2.CV_64F,1,0)\n",
    "scharry = cv2.Scharr(img,cv2.CV_64F,0,1)\n",
    "scharrx = cv2.convertScaleAbs(scharrx)\n",
    "scharry = cv2.convertScaleAbs(scharry)\n",
    "scharrxy = cv2.addWeighted(scharrx,0.5,scharry,0.5,0)\n",
    "\n",
    "laplacian = cv2.Laplacian(img,cv2.CV_64F) # 没有 x、y，因为是求周围点的比较\n",
    "laplacian = cv2.convertScaleAbs(laplacian)\n",
    "\n",
    "res = np.hstack((sobelxy,scharrxy,laplacian))\n",
    "cv_show(res,'res')"
   ]
  }
 ],
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