Image_processing_tips.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备空白页面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = np.zeros((200, 300), dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用OpenCV画矩形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iHZz7CNq9HhpfhQNZrB/Nj2NVHayqF6rqReBWfvHRuqm+JVnNbLh9tqq+0DU3P24L9WuljFk/xh3cDwEbk2xIcjKwGbhnzDX1LcmpSU6bWwfeBuxmtk/XdYddB3xpPBUObLF+3AO8q3tK4U3Aj3o+mjdh3tzu1cyOG8z2bXOSNUk2ABuBb/yq61uOJAE+DTxWVR/v2dX0uC3Wr5UwZn0b919Hmf3L9hPM/uX3w+OuZ8C+nM/sX7O/DTwy1x/gFcB24Eng34G14651GX3ZxuzHz6PMzhFev1g/mH0q4R+6MfwOMD3u+vvo2z91te9i9j/89T3Hf7jr2+PAleOu/xj9ejOz0yC7gJ3dclXr43aMfjU/Zv0ufnNSkhoz7qkSSdJxMrglqTEGtyQ1xuCWpMYY3JLUGINbkhpjcEtSYwxuSWrM/wFA6kyuXZHVaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = 40\n",
    "y1 = 60\n",
    "x2 = 100\n",
    "y2 = 150\n",
    "img2 = cv2.rectangle(img.copy(), (x1, y1), (x2, y2), (255), -1)\n",
    "plt.imshow(img2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用matplotlib画矩形并添加文字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.imshow(img)\n",
    "\n",
    "x = 100\n",
    "y = 40\n",
    "w = 50\n",
    "h = 60\n",
    "\n",
    "ax.add_patch( plt.Rectangle((x, y), w, y, fill=False, edgecolor='red', linewidth=3.5) )\n",
    "ax.text(x, y - 5, 'AAAAA', bbox=dict(facecolor='blue', alpha=0.5), fontsize=14, color='white')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用matplotlib画多边形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.imshow(img)\n",
    "\n",
    "ax.add_patch(plt.Polygon([[50, 40], [150, 40], [160, 100], [40, 110], [70, 70]], fill=False, color=\"y\"))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用matplotlib显示多个图像（方法一）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tate = 1\n",
    "yoko = 2\n",
    "plt.subplot(tate, yoko, 1)\n",
    "plt.imshow(img)\n",
    "plt.subplot(tate, yoko, 2)\n",
    "plt.imshow(img2)\n",
    "plt.subplots_adjust(left=0.075, bottom=0.05, right=0.95, top=0.95, wspace=0.15, hspace=0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用matplotlib显示多个图像（方法二）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tate = 1\n",
    "yoko = 2\n",
    "fig, axs = plt.subplots(tate, yoko)\n",
    "axs[0].imshow(img)\n",
    "axs[1].imshow(img2)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}