创建数据库.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee8fdeaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import requests\n",
    "import urllib3\n",
    "urllib3.disable_warnings()\n",
    "from tqdm import tqdm #进度条库\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bbd7f615",
   "metadata": {},
   "outputs": [],
   "source": [
    "def craw_single_class(keyword, DOWNLOAD_NUM = 50):\n",
    "    \"\"\"\n",
    "    参数说明：\n",
    "    keyword: 爬取对象\n",
    "    DOWNLOAD_NUM:爬取的数量\n",
    "    \"\"\"\n",
    "    ########################HTTP请求参数###############################\n",
    "    cookies = {\n",
    "    'BDqhfp': '%E7%8B%97%E7%8B%97%26%26NaN-1undefined%26%2618880%26%2621',\n",
    "    'BIDUPSID': '06338E0BE23C6ADB52165ACEB972355B',\n",
    "    'PSTM': '1646905430',\n",
    "    'BAIDUID': '104BD58A7C408DABABCAC9E0A1B184B4:FG=1',\n",
    "    'BDORZ': 'B490B5EBF6F3CD402E515D22BCDA1598',\n",
    "    'H_PS_PSSID': '35836_35105_31254_36024_36005_34584_36142_36120_36032_35993_35984_35319_26350_35723_22160_36061',\n",
    "    'BDSFRCVID': '8--OJexroG0xMovDbuOS5T78igKKHJQTDYLtOwXPsp3LGJLVgaSTEG0PtjcEHMA-2ZlgogKK02OTH6KF_2uxOjjg8UtVJeC6EG0Ptf8g0M5',\n",
    "    'H_BDCLCKID_SF': 'tJPqoKtbtDI3fP36qR3KhPt8Kpby2D62aKDs2nopBhcqEIL4QTQM5p5yQ2c7LUvtynT2KJnz3Po8MUbSj4QoDjFjXJ7RJRJbK6vwKJ5s5h5nhMJSb67JDMP0-4F8exry523ioIovQpn0MhQ3DRoWXPIqbN7P-p5Z5mAqKl0MLPbtbb0xXj_0D6bBjHujtT_s2TTKLPK8fCnBDP59MDTjhPrMypomWMT-0bFH_-5L-l5js56SbU5hW5LSQxQ3QhLDQNn7_JjOX-0bVIj6Wl_-etP3yarQhxQxtNRdXInjtpvhHR38MpbobUPUDa59LUvEJgcdot5yBbc8eIna5hjkbfJBQttjQn3hfIkj0DKLtD8bMC-RDjt35n-Wqxobbtof-KOhLTrJaDkWsx7Oy4oTj6DD5lrG0P6RHmb8ht59JROPSU7mhqb_3MvB-fnEbf7r-2TP_R6GBPQtqMbIQft20-DIeMtjBMJaJRCqWR7jWhk2hl72ybCMQlRX5q79atTMfNTJ-qcH0KQpsIJM5-DWbT8EjHCet5DJJn4j_Dv5b-0aKRcY-tT5M-Lf5eT22-usy6Qd2hcH0KLKDh6gb4PhQKuZ5qutLTb4QTbqWKJcKfb1MRjvMPnF-tKZDb-JXtr92nuDal5TtUthSDnTDMRhXfIL04nyKMnitnr9-pnLJpQrh459XP68bTkA5bjZKxtq3mkjbPbDfn02eCKuj6tWj6j0DNRabK6aKC5bL6rJabC3b5CzXU6q2bDeQN3OW4Rq3Irt2M8aQI0WjJ3oyU7k0q0vWtvJWbbvLT7johRTWqR4enjb3MonDh83Mxb4BUrCHRrzWn3O5hvvhKoO3MA-yUKmDloOW-TB5bbPLUQF5l8-sq0x0bOte-bQXH_E5bj2qRCqVIKa3f',\n",
    "    'BDSFRCVID_BFESS': '8--OJexroG0xMovDbuOS5T78igKKHJQTDYLtOwXPsp3LGJLVgaSTEG0PtjcEHMA-2ZlgogKK02OTH6KF_2uxOjjg8UtVJeC6EG0Ptf8g0M5',\n",
    "    'H_BDCLCKID_SF_BFESS': 'tJPqoKtbtDI3fP36qR3KhPt8Kpby2D62aKDs2nopBhcqEIL4QTQM5p5yQ2c7LUvtynT2KJnz3Po8MUbSj4QoDjFjXJ7RJRJbK6vwKJ5s5h5nhMJSb67JDMP0-4F8exry523ioIovQpn0MhQ3DRoWXPIqbN7P-p5Z5mAqKl0MLPbtbb0xXj_0D6bBjHujtT_s2TTKLPK8fCnBDP59MDTjhPrMypomWMT-0bFH_-5L-l5js56SbU5hW5LSQxQ3QhLDQNn7_JjOX-0bVIj6Wl_-etP3yarQhxQxtNRdXInjtpvhHR38MpbobUPUDa59LUvEJgcdot5yBbc8eIna5hjkbfJBQttjQn3hfIkj0DKLtD8bMC-RDjt35n-Wqxobbtof-KOhLTrJaDkWsx7Oy4oTj6DD5lrG0P6RHmb8ht59JROPSU7mhqb_3MvB-fnEbf7r-2TP_R6GBPQtqMbIQft20-DIeMtjBMJaJRCqWR7jWhk2hl72ybCMQlRX5q79atTMfNTJ-qcH0KQpsIJM5-DWbT8EjHCet5DJJn4j_Dv5b-0aKRcY-tT5M-Lf5eT22-usy6Qd2hcH0KLKDh6gb4PhQKuZ5qutLTb4QTbqWKJcKfb1MRjvMPnF-tKZDb-JXtr92nuDal5TtUthSDnTDMRhXfIL04nyKMnitnr9-pnLJpQrh459XP68bTkA5bjZKxtq3mkjbPbDfn02eCKuj6tWj6j0DNRabK6aKC5bL6rJabC3b5CzXU6q2bDeQN3OW4Rq3Irt2M8aQI0WjJ3oyU7k0q0vWtvJWbbvLT7johRTWqR4enjb3MonDh83Mxb4BUrCHRrzWn3O5hvvhKoO3MA-yUKmDloOW-TB5bbPLUQF5l8-sq0x0bOte-bQXH_E5bj2qRCqVIKa3f',\n",
    "    'indexPageSugList': '%5B%22%E7%8B%97%E7%8B%97%22%5D',\n",
    "    'cleanHistoryStatus': '0',\n",
    "    'BAIDUID_BFESS': '104BD58A7C408DABABCAC9E0A1B184B4:FG=1',\n",
    "    'BDRCVFR[dG2JNJb_ajR]': 'mk3SLVN4HKm',\n",
    "    'BDRCVFR[-pGxjrCMryR]': 'mk3SLVN4HKm',\n",
    "    'ab_sr': '1.0.1_Y2YxZDkwMWZkMmY2MzA4MGU0OTNhMzVlNTcwMmM2MWE4YWU4OTc1ZjZmZDM2N2RjYmVkMzFiY2NjNWM4Nzk4NzBlZTliYWU0ZTAyODkzNDA3YzNiMTVjMTllMzQ0MGJlZjAwYzk5MDdjNWM0MzJmMDdhOWNhYTZhMjIwODc5MDMxN2QyMmE1YTFmN2QyY2M1M2VmZDkzMjMyOThiYmNhZA==',\n",
    "    'delPer': '0',\n",
    "    'PSINO': '2',\n",
    "    'BA_HECTOR': '8h24a024042g05alup1h3g0aq0q',\n",
    "    }\n",
    "\n",
    "    headers = {\n",
    "    'Connection': 'keep-alive',\n",
    "    'sec-ch-ua': '\" Not;A Brand\";v=\"99\", \"Google Chrome\";v=\"97\", \"Chromium\";v=\"97\"',\n",
    "    'Accept': 'text/plain, */*; q=0.01',\n",
    "    'X-Requested-With': 'XMLHttpRequest',\n",
    "    'sec-ch-ua-mobile': '?0',\n",
    "    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.99 Safari/537.36',\n",
    "    'sec-ch-ua-platform': '\"macOS\"',\n",
    "    'Sec-Fetch-Site': 'same-origin',\n",
    "    'Sec-Fetch-Mode': 'cors',\n",
    "    'Sec-Fetch-Dest': 'empty',\n",
    "    'Referer': 'https://image.baidu.com/search/index?tn=baiduimage&ipn=r&ct=201326592&cl=2&lm=-1&st=-1&fm=result&fr=&sf=1&fmq=1647837998851_R&pv=&ic=&nc=1&z=&hd=&latest=&copyright=&se=1&showtab=0&fb=0&width=&height=&face=0&istype=2&dyTabStr=MCwzLDIsNiwxLDUsNCw4LDcsOQ%3D%3D&ie=utf-8&sid=&word=%E7%8B%97%E7%8B%97',\n",
    "    'Accept-Language': 'zh-CN,zh;q=0.9',\n",
    "    }\n",
    "    ############################创建文件夹################################\n",
    "    if os.path.exists('dataset/'+keyword):\n",
    "        print('文件夹 dataset/{} 已存在，之后直接将爬取到的图片保存至该文件夹中'.format(keyword))\n",
    "    else:\n",
    "        os.makedirs('dataset/{}'.format(keyword))\n",
    "        print('新建文件夹：dataset/{}'.format(keyword))\n",
    "    #####################爬取并保存图像文件至本地#########################\n",
    "    count = 1\n",
    "    with tqdm(total=DOWNLOAD_NUM, position=0, leave=True) as pbar:\n",
    "        num = 0 #爬取第几张\n",
    "        FLAG = True #是否继续爬取\n",
    "        while FLAG:\n",
    "            page = 30 * count\n",
    "            params = (\n",
    "                ('tn', 'resultjson_com'),\n",
    "                ('logid', '12508239107856075440'),\n",
    "                ('ipn', 'rj'),\n",
    "                ('ct', '201326592'),\n",
    "                ('is', ''),\n",
    "                ('fp', 'result'),\n",
    "                ('fr', ''),\n",
    "                ('word', f'{keyword}'),\n",
    "                ('queryWord', f'{keyword}'),\n",
    "                ('cl', '2'),\n",
    "                ('lm', '-1'),\n",
    "                ('ie', 'utf-8'),\n",
    "                ('oe', 'utf-8'),\n",
    "                ('adpicid', ''),\n",
    "                ('st', '-1'),\n",
    "                ('z', ''),\n",
    "                ('ic', ''),\n",
    "                ('hd', ''),\n",
    "                ('latest', ''),\n",
    "                ('copyright', ''),\n",
    "                ('s', ''),\n",
    "                ('se', ''),\n",
    "                ('tab', ''),\n",
    "                ('width', ''),\n",
    "                ('height', ''),\n",
    "                ('face', '0'),\n",
    "                ('istype', '2'),\n",
    "                ('qc', ''),\n",
    "                ('nc', '1'),\n",
    "                ('expermode', ''),\n",
    "                ('nojc', ''),\n",
    "                ('isAsync', ''),\n",
    "                ('pn', f'{page}'),\n",
    "                ('rn', '30'),\n",
    "                ('gsm', '1e'),\n",
    "                ('1647838001666', ''),\n",
    "            )\n",
    "            response = requests.get('https://image.baidu.com/search/acjson', headers=headers, params=params, cookies=cookies)\n",
    "            if response.status_code == 200:\n",
    "                try:\n",
    "                    json_data = response.json().get(\"data\")\n",
    "\n",
    "                    if json_data:\n",
    "                        for x in json_data:\n",
    "                            type = x.get(\"type\")\n",
    "                            if type not in [\"gif\"]:#剔除gif格式的图片\n",
    "                                img = x.get(\"thumbURL\")\n",
    "                                fromPageTitleEnc = x.get(\"fromPageTitleEnc\")\n",
    "                                try:\n",
    "                                    resp = requests.get(url=img, verify=False)\n",
    "                                    time.sleep(1)\n",
    "                                    # print(f\"链接 {img}\")\n",
    "\n",
    "                                    # 保存文件名\n",
    "                                    # file_save_path = f'dataset/{keyword}/{num}-{fromPageTitleEnc}.{type}'\n",
    "                                    file_save_path = f'dataset/{keyword}/{num}.{type}'\n",
    "                                    with open(file_save_path, 'wb') as f:                                    \n",
    "                                        f.write(resp.content)\n",
    "                                        f.flush()\n",
    "                                        # print('第 {} 张图像 {} 爬取完成'.format(num, fromPageTitleEnc))\n",
    "                                        num += 1\n",
    "                                        pbar.update(1) # 进度条更新\n",
    "\n",
    "                                    # 爬取数量达到要求\n",
    "                                    if num > DOWNLOAD_NUM:\n",
    "                                        FLAG = False\n",
    "                                        print('{} 张图像爬取完毕'.format(num))\n",
    "                                        break\n",
    "\n",
    "                                except Exception:\n",
    "                                    pass\n",
    "                except:\n",
    "                    pass\n",
    "            else:\n",
    "                break\n",
    "\n",
    "            count += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e489f286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文件夹 dataset/梅花肉 已存在，之后直接将爬取到的图片保存至该文件夹中\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "501it [11:42,  1.40s/it]                                                                                               "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "501 张图像爬取完毕\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "craw_single_class('梅花肉', DOWNLOAD_NUM = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "62395211",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_1292\\244782893.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m             \u001b[1;31m#df = df.concat({'类别':meat, '文件名':file, '图像宽':img.shape[1], '图像高':img.shape[0]}, ignore_index=True)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m             \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m             \u001b[0mw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_1292\\244782893.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     23\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmeat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'读取错误'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m     \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object is not callable"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 指定数据集路径\n",
    "dataset_path = 'C:/Users/beichen/dataset/meihuameat'\n",
    "os.chdir(dataset_path)\n",
    "#print(os.listdir())\n",
    "\n",
    "df = pd.DataFrame()\n",
    "for file in os.listdir(): # 遍历每张图像\n",
    "    try:\n",
    "            img = cv2.imread(file)\n",
    "            #df = df.concat({'类别':meat, '文件名':file, '图像宽':img.shape[1], '图像高':img.shape[0]}, ignore_index=True)\n",
    "            size = img.shape\n",
    "            w = size[1]\n",
    "            h = size[0]\n",
    "            print=(file,size,w,h)\n",
    "    except:\n",
    "        print(os.path.join(meat, file), '读取错误')\n",
    "    os.chdir('../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a07bbc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-contrib-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "19656e2b",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'图像宽'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3628\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3629\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3630\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '图像宽'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_1292\\3244112969.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolors\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLogNorm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'图像宽'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'图像高'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3503\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3504\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3505\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3506\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3507\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3629\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3630\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3631\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3632\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3633\u001b[0m                 \u001b[1;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '图像宽'"
     ]
    }
   ],
   "source": [
    "from scipy.stats import gaussian_kde\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "x = df['图像宽']\n",
    "y = df['图像高']\n",
    "\n",
    "xy = np.vstack([x,y])\n",
    "z = gaussian_kde(xy)(xy)\n",
    "\n",
    "# Sort the points by density, so that the densest points are plotted last\n",
    "idx = z.argsort()\n",
    "x, y, z = x[idx], y[idx], z[idx]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "# plt.figure(figsize=(12,12))\n",
    "plt.scatter(x, y, c=z,  s=5, cmap='Spectral_r')\n",
    "# plt.colorbar()\n",
    "# plt.xticks([])\n",
    "# plt.yticks([])\n",
    "\n",
    "plt.tick_params(labelsize=15)\n",
    "\n",
    "xy_max = max(max(df['图像宽']), max(df['图像高']))\n",
    "plt.xlim(xmin=0, xmax=xy_max)\n",
    "plt.ylim(ymin=0, ymax=xy_max)\n",
    "\n",
    "plt.ylabel('height', fontsize=25)\n",
    "plt.xlabel('width', fontsize=25)\n",
    "\n",
    "plt.savefig('图像尺寸分布.pdf', dpi=120, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2a574950",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                          | 0/501 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'tuple' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_1292\\1342182686.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     24\u001b[0m             \u001b[0mh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object is not callable",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_1292\\1342182686.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfruit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'读取错误'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m     \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object is not callable"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 指定数据集路径\n",
    "dataset_path = 'C:/Users/beichen/dataset/meihuameat'\n",
    "os.chdir(dataset_path)\n",
    "#print(os.listdir())\n",
    "\n",
    "df = pd.DataFrame()\n",
    "for fruit in tqdm(os.listdir()): # 遍历每个类别    \n",
    "    #os.chdir(fruit)\n",
    "    for file in os.listdir(): # 遍历每张图像\n",
    "        try:\n",
    "            img = cv2.imread(file)\n",
    "            #df = df.append({'类别':fruit, '文件名':file, '图像宽':img.shape[1], '图像高':img.shape[0]}, ignore_index=True)\n",
    "            size = img.shape\n",
    "            w = size[1]\n",
    "            h = size[0]\n",
    "            print(file,size,w,h)\n",
    "        except:\n",
    "            print(os.path.join(fruit, file), '读取错误')\n",
    "    os.chdir('../')\n",
    "os.chdir('../')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e531062",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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