图片配对 HTML替换.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "import imagehash\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图片配对（废弃）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r'D:\\workspace\\TEMP\\bys_single\\images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgpool = []\n",
    "imghashs = np.array([])\n",
    "for imgfile in os.listdir():\n",
    "    imgpool.append(imgfile)\n",
    "    thash = imagehash.average_hash(Image.open(imgfile))\n",
    "    imghashs = np.append(imghashs,thash)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohash = imagehash.average_hash(Image.open('../test/xjj.jpg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'005.jfif'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgpool[np.argmin(imghashs - ohash)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "imghashs = np.array(imghashs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 更换img src"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r'D:\\workspace\\TEMP\\bys_single\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# with open(\"file\", 'wb') as fp:\n",
    "#     pickle.dump(files, fp)\n",
    "with open(\"file\", 'rb') as fp:\n",
    "    files = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "for file in files:\n",
    "    with open(file, 'rb') as fp:\n",
    "        bso = BeautifulSoup(fp.read().decode())\n",
    "        for div in bso.find_all('div', {'class':'ubook_img'}):\n",
    "            div.img['src'] = div.img['img-src']\n",
    "            \n",
    "    with open(file, 'wb') as fp:\n",
    "        fp.write(bso.encode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('5-2.html', 'wb+') as fp:\n",
    "    bso = BeautifulSoup(fp.read().decode())\n",
    "    for div in bso.find_all('div', {'class':'ubook_img'}):\n",
    "        div.img['src'] = div.img['img-src']\n",
    "    fp.seek(0)\n",
    "    fp.write(bso.encode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'asd123'\n"
     ]
    }
   ],
   "source": [
    "with open('5-1.html', 'rb+') as fp:\n",
    "    print(fp.read())\n",
    "    fp.seek(0)\n",
    "    fp.write(b'123')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 2):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "b.append(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "b.append([2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.append([2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n"
     ]
    }
   ],
   "source": [
    "a = []\n",
    "b = []\n",
    "for i in range(3):\n",
    "#     a = []\n",
    "    a.append(1)\n",
    "    b.append(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1], [1], [1]]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "py35",
   "language": "python",
   "name": "py35"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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