加入紫边，分离模型

02_classification.ipynb

@@ -2,21 +2,17 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "source": [],
    "metadata": {
+    "collapsed": false,
     "pycharm": {
      "name": "#%% md\n"
     }
-   },
+   }
-   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -24,7 +20,13 @@
     "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",
@@ -35,8 +37,11 @@
     "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'} # 颜色对应的类别\n",
+    "# color_dict = {(0, 0, 255): \"yangeng\", (255, 0, 0): 'beijing'} # 颜色对应的类别\n",
-    "label_index = {\"yangeng\": 1, \"beijing\": 0} # 类别对应的序号\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",
@@ -57,26 +62,16 @@
     "## 读取数据"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
-   "outputs": [
+   "outputs": [],
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'data/dataset/label'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
-      "\u001B[0;32m/var/folders/wh/kr5c3dr12834pfk3j7yqnrq40000gn/T/ipykernel_30867/1942905945.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mdataset\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mread_labeled_img\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata_dir\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcolor_dict\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mcolor_dict\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mis_ps_color_space\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      2\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mshow_samples\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m     \u001B[0;32mfrom\u001B[0m \u001B[0mutils\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mlab_scatter\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m     \u001B[0mlab_scatter\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mclass_max_num\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m30000\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mis_3d\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mis_ps_color_space\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;32m~/PycharmProjects/tobacco_color/utils.py\u001B[0m in \u001B[0;36mread_labeled_img\u001B[0;34m(dataset_dir, color_dict, ext, is_ps_color_space)\u001B[0m\n\u001B[1;32m     37\u001B[0m     \u001B[0;34m:\u001B[0m\u001B[0;32mreturn\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0m字典形式的数据集\u001B[0m\u001B[0;34m{\u001B[0m\u001B[0mlabel\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mvector\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mn\u001B[0m \u001B[0mx\u001B[0m \u001B[0;36m3\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0mvector为lab色彩空间\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     38\u001B[0m     \"\"\"\n\u001B[0;32m---> 39\u001B[0;31m     img_names = [img_name for img_name in os.listdir(os.path.join(dataset_dir, 'label'))\n\u001B[0m\u001B[1;32m     40\u001B[0m                  if img_name.endswith(ext)]\n\u001B[1;32m     41\u001B[0m     \u001B[0mtotal_dataset\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mMergeDict\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: 'data/dataset/label'"
-     ]
-    }
-   ],
    "source": [
     "dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)\n",
     "if show_samples:\n",
@@ -96,25 +91,16 @@
     "## 数据平衡化"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
-   "outputs": [
+   "outputs": [],
-    {
-     "ename": "NameError",
-     "evalue": "name 'dataset' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
-      "\u001B[0;32m/var/folders/wh/kr5c3dr12834pfk3j7yqnrq40000gn/T/ipykernel_30867/603974095.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0mrus\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mRandomUnderSampler\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrandom_state\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mx_list\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_list\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconcatenate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mv\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mk\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mv\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtolist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m                  \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconcatenate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mones\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mv\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0mlabel_index\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mk\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mk\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mv\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtolist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0mx_resampled\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_resampled\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mrus\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit_resample\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx_list\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_list\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mdataset\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0;34m\"inside\"\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx_resampled\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;31mNameError\u001B[0m: name 'dataset' is not defined"
-     ]
-    }
-   ],
    "source": [
     "rus = RandomUnderSampler(random_state=0)\n",
     "x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \\\n",
@@ -135,25 +121,16 @@
     "## 模型训练"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
-   "outputs": [
+   "outputs": [],
-    {
-     "ename": "NameError",
-     "evalue": "name 'dataset' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
-      "\u001B[0;32m/var/folders/wh/kr5c3dr12834pfk3j7yqnrq40000gn/T/ipykernel_30867/1828483636.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m# 对数据进行预处理\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconcatenate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mv\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mk\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mv\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m \u001B[0mnegative_sample_num\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0;36m1.2\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mnegative_sample_num\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m \u001B[0;32melse\u001B[0m \u001B[0mnegative_sample_num\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mAnonymousColorDetector\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;31mNameError\u001B[0m: name 'dataset' is not defined"
-     ]
-    }
-   ],
    "source": [
     "# 对数据进行预处理\n",
     "x = np.concatenate([v for k, v in dataset.items()], axis=0)\n",
@@ -167,6 +144,28 @@
     }
    }
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "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"
+    }
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": 14,

02_classification.py

@@ -1,53 +1,66 @@
-#!/usr/bin/env python
+# %%
-# coding: utf-8
-
-# # 模型的训练
-
-# In[16]:
-
-
 import numpy as np
 import scipy
 from imblearn.under_sampling import RandomUnderSampler
 from models import AnonymousColorDetector
 from utils import read_labeled_img
 
-# ## 读取数据与构建数据集
+# %%
+train_from_existed = True  # 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
+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
+dataset_file = "data/dataset/dataset_2022-07-20_10-04.mat"
 
-# In[17]:
+# color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing'} # 颜色对应的类别
+color_dict = {(0, 0, 255): "yangeng"}
+color_dict = {(255, 0, 0): 'beijing'}
+color_dict = {(0, 255, 0): "zibian"}
+label_index = {"yangeng": 1, "beijing": 0, "zibian": 2}  # 类别对应的序号
+show_samples = False  # 是否展示样本
 
-
+# 定义一些训练量
-data_dir = "data/dataset"
+threshold = 5  # 正样本周围多大范围内的还算是正样本
-color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing'}
+node_num = 20  # 如果使用ELM作为分类器物，有多少的节点
-label_index = {"yangeng": 1, "beijing": 0}
+negative_sample_num = None  # None或者一个数字，对应生成的负样本数量
+# %% md
+## 读取数据
+# %%
 dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)
+if show_samples:
+    from utils import lab_scatter
+
+    lab_scatter(dataset, class_max_num=30000, is_3d=True, is_ps_color_space=False)
+# %% md
+## 数据平衡化
+# %%
 rus = RandomUnderSampler(random_state=0)
 x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \
                  np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()], axis=0).tolist()
-
 x_resampled, y_resampled = rus.fit_resample(x_list, y_list)
 dataset = {"inside": np.array(x_resampled)}
-
+# %% md
-# ## 模型训练
+## 模型训练
-
+# %%
-# In[18]:
-
-
-# 定义一些常量
-threshold = 5
-node_num = 20
-negative_sample_num = None  # None或者一个数字
-world_boundary = np.array([0, 0, 0, 255, 255, 255])
 # 对数据进行预处理
 x = np.concatenate([v for k, v in dataset.items()], axis=0)
 negative_sample_num = int(x.shape[0] * 1.2) if negative_sample_num is None else negative_sample_num
-
 model = AnonymousColorDetector()
-
+# %%
-model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
+if train_from_existed:
-          is_save_dataset=True, model_selection='dt')
+    data = scipy.io.loadmat(dataset_file)
-# data = scipy.io.loadmat('dataset_2022-07-19_15-07.mat')
+    x, y = data['x'], data['y'].ravel()
-# x, y = data['x'], data['y'].ravel()
+    model.fit(x, y=y, is_generate_negative=False, model_selection='dt')
-# model.fit(x, y=y, is_generate_negative=False, model_selection='dt')
+else:
-
+    world_boundary = np.array([0, 0, 0, 255, 255, 255])
+    model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
+              is_save_dataset=True, model_selection='dt')
+model.save()
+# %%
+if train_from_existed:
+    data = scipy.io.loadmat(dataset_file)
+    x, y = data['x'], data['y'].ravel()
+    model.fit(x, y=y, is_generate_negative=False, model_selection='dt')
+else:
+    world_boundary = np.array([0, 0, 0, 255, 255, 255])
+    model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
+              is_save_dataset=True, model_selection='dt')
 model.save()