添加了像素点模型的训练代码

05_model_training.ipynb

@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "outputs": [],
    "source": [
     "data_path = r'data/envi20220802.txt'\n",
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "outputs": [],
    "source": [
     "data = read_envi_ascii(data_path)"
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 4,
    "outputs": [
     {
      "name": "stdout",
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 5,
    "outputs": [],
    "source": [
     "data_x = [d for class_name, d in data.items() if class_name in name_dict.keys()]\n",
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 6,
    "outputs": [
     {
      "name": "stdout",
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 7,
    "outputs": [
     {
      "name": "stdout",
@@ -205,7 +205,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "#"
+    "# 进行模型训练\n",
+    "分出一部分数据进行训练"
    ],
    "metadata": {
     "collapsed": false,
@@ -213,6 +214,237 @@
      "name": "#%% md\n"
     }
    }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [],
+   "source": [
+    "from models import DecisionTree\n",
+    "from sklearn.model_selection import train_test_split"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [],
+   "source": [
+    "train_x, test_x, train_y, test_y = train_test_split(x_resampled, y_resampled, test_size=0.2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "tree = DecisionTree(class_weight={1:20})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [],
+   "source": [
+    "tree = tree.fit(train_x, train_y)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 模型评估"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 多分类精度"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [],
+   "source": [
+    "pred_y = tree.predict(test_x)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       1.00      1.00      1.00       304\n",
+      "         1.0       0.97      0.98      0.97       275\n",
+      "         2.0       0.98      1.00      0.99       297\n",
+      "         3.0       0.95      0.99      0.97       293\n",
+      "         4.0       0.98      0.91      0.95       316\n",
+      "         5.0       0.98      0.98      0.98       264\n",
+      "\n",
+      "    accuracy                           0.98      1749\n",
+      "   macro avg       0.98      0.98      0.98      1749\n",
+      "weighted avg       0.98      0.98      0.98      1749\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "print(classification_report(y_pred=pred_y, y_true=test_y))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 二分类精度"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [],
+   "source": [
+    "test_y[test_y <= 1] = 0\n",
+    "test_y[test_y > 1] = 1\n",
+    "pred_y = tree.predict_bin(test_x)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       1.00      1.00      1.00       582\n",
+      "         1.0       1.00      1.00      1.00      1167\n",
+      "\n",
+      "    accuracy                           1.00      1749\n",
+      "   macro avg       1.00      1.00      1.00      1749\n",
+      "weighted avg       1.00      1.00      1.00      1749\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_true=pred_y, y_pred=pred_y))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 模型保存"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "path = datetime.datetime.now().strftime(f\"models/pixel_%Y-%m-%d_%H-%M.model\")\n",
+    "with open(path, 'wb') as f:\n",
+    "    pickle.dump(tree, f)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {

README.md

@@ -175,3 +175,30 @@
 偏差的影响，也可从这幅图当中看到，这幅图的上下偏差达到了惊人的200像素，明显考虑是触发有问题了，不然偏差值至少是恒定的。
 
 结论是考虑RGB相机的触发存在一定问题。
+
+## 喷阀检查
+
+为了能够有效的对喷阀进行检查，我写了一个用于测试的小socket，这个小socket的使用方式是这样的：
+
+开启服务端：
+
+```shel
+python valve_test.py 
+```
+
+然后按照要求进行输入就可以了，我还在里头藏了个彩蛋，你猜猜是啥。
+
+如果想要开客户端，可以加个参数，就像这样：
+
+```shel
+python valve_test.py -c
+```
+
+这个客户端啥也不会干，只会做去显示相应的收到的指令。
+
+同时运行这两个可以在本地看到测试结果，不用看zynq那边的结果：
+
+![截屏2022-08-02 14.16.24](https://raw.githubusercontent.com/Karllzy/imagebed/main/img/%E6%88%AA%E5%B1%8F2022-08-02%2014.16.24.png)
+
+
+

models.py

@@ -255,7 +255,7 @@ class ManualTree:
 
 # 机器学习像素模型类
 class PixelModelML:
-    def __init__(self, pixel_model_path):
+    def __init__(self, pixel_model_path=None):
         with open(pixel_model_path, "rb") as f:
             self.dt = pickle.load(f)
 
@@ -409,7 +409,7 @@ class SpecDetector(Detector):
         if x_yellow.shape[0] == 0:
             return non_yellow_things
         else:
-            tobacco = self.pixel_model_ml.predict(x_yellow[..., Config.green_bands]) > 0.5
+            tobacco = self.pixel_model_ml.predict_bin(x_yellow) < 0.5
 
             non_yellow_things[yellow_things] = ~tobacco
             # 杂质mask中将背景赋值为0,将杂质赋值为1
@@ -436,6 +436,14 @@ class SpecDetector(Detector):
         return blk_result_array
 
 
+class DecisionTree(DecisionTreeClassifier):
+    def predict_bin(self, feature):
+        res = self.predict(feature)
+        res[res <= 1] = 0
+        res[res > 1] = 1
+        return res
+
+
 if __name__ == '__main__':
     data_dir = "data/dataset"
     color_dict = {(0, 0, 255): "yangeng"}

valve_test.py

@@ -154,7 +154,7 @@ d. 阀板的脉冲分频系数,>=2即可                          h. 发个da和
 class VirtualValve:
     def __init__(self):
         self.client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)  # 声明socket类型，同时生成链接对象
-        self.client.connect(('localhost', 13452))  # 建立一个链接，连接到本地的6969端口
+        self.client.connect(('localhost', 13452))  # 建立一个链接，连接到本地的13452端口
 
     def run(self):
         while True:
@@ -166,7 +166,6 @@ class VirtualValve:
 
 if __name__ == '__main__':
     import argparse
-
     parser = argparse.ArgumentParser(description='阀门测程序')
     parser.add_argument('-c', default=False, action='store_true', help='是否是开个客户端', required=False)
     args = parser.parse_args()