{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from models import AnonymousColorDetector\n",
    "from utils import read_labeled_img"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "train_from_existed = False # 是否从现有数据训练，如果是的话，那就从dataset_file训练，否则就用data_dir里头的数据\n",
    "data_dir = \"data/dataset\" # 数据集，文件夹下必须包含`img`和`label`两个文件夹，放置相同文件名的图片和label\n",
    "dataset_file = \"data/dataset/dataset_2022-07-20_10-04.mat\"\n",
    "\n",
    "color_dict = {(0, 0, 255): \"yangeng\", (255, 0, 0): 'beijing',(0, 255, 0): \"zibian\"} # 颜色对应的类别\n",
    "# color_dict = {(0, 0, 255): \"yangeng\"}\n",
    "# color_dict = {(255, 0, 0): 'beijing'}\n",
    "# color_dict = {(0, 255, 0): \"zibian\"}\n",
    "label_index = {\"yangeng\": 1, \"beijing\": 0, \"zibian\":2} # 类别对应的序号\n",
    "show_samples = False # 是否展示样本\n",
    "\n",
    "# 定义一些训练量\n",
    "threshold = 5  # 正样本周围多大范围内的还算是正样本\n",
    "node_num = 20  # 如果使用ELM作为分类器物，有多少的节点\n",
    "negative_sample_num = None  # None或者一个数字，对应生成的负样本数量"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 读取数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)\n",
    "if show_samples:\n",
    "    from utils import lab_scatter\n",
    "    lab_scatter(dataset, class_max_num=30000, is_3d=True, is_ps_color_space=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 数据平衡化"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "if len(dataset) > 1:\n",
    "    rus = RandomUnderSampler(random_state=0)\n",
    "    x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \\\n",
    "                     np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()], axis=0).tolist()\n",
    "    x_resampled, y_resampled = rus.fit_resample(x_list, y_list)\n",
    "    dataset = {\"inside\": np.array(x_resampled)}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 模型训练"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "# 对数据进行预处理\n",
    "x = np.concatenate([v for k, v in dataset.items()], axis=0)\n",
    "negative_sample_num = int(x.shape[0] * 1.2) if negative_sample_num is None else negative_sample_num\n",
    "model = AnonymousColorDetector()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1923it [00:00, 5114.42it/s]                                                                         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       577\n",
      "           1       0.99      0.99      0.99       481\n",
      "\n",
      "    accuracy                           0.99      1058\n",
      "   macro avg       0.99      0.99      0.99      1058\n",
      "weighted avg       0.99      0.99      0.99      1058\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "if train_from_existed:\n",
    "    data = scipy.io.loadmat(dataset_file)\n",
    "    x, y = data['x'], data['y'].ravel()\n",
    "    model.fit(x, y=y, is_generate_negative=False, model_selection='dt')\n",
    "else:\n",
    "    world_boundary = np.array([0, 0, 0, 255, 255, 255])\n",
    "    model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,\n",
    "          is_save_dataset=True, model_selection='dt')\n",
    "model.save()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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