5장. 2절. 싸이킷런의 MLPClassifier를 이용한 실시간 손글씨 숫자 인식하기.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-10T01:11:21.146667Z",
     "start_time": "2021-06-10T01:11:21.085687Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 2000)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "img = cv2.imread('./images/digits.png', cv2.IMREAD_GRAYSCALE)\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-10T01:11:54.366569Z",
     "start_time": "2021-06-10T01:11:54.334566Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "cells = [np.hsplit(row,100) for row in np.vsplit(img,50)]\n",
    "x = np.array(cells) / 255.\n",
    "X = x[:,:].reshape(-1,400).astype(np.float32)\n",
    "y = np.repeat(np.arange(10),500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-10T01:12:03.994458Z",
     "start_time": "2021-06-10T01:12:01.657236Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n",
    "                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 레이어 정의"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-10T01:13:19.866279Z",
     "start_time": "2021-06-10T01:13:19.848273Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "model = MLPClassifier(hidden_layer_sizes=(512,256,128), verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-10T01:13:33.017021Z",
     "start_time": "2021-06-10T01:13:27.053978Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.30969835\n",
      "Iteration 2, loss = 0.40384133\n",
      "Iteration 3, loss = 0.25961665\n",
      "Iteration 4, loss = 0.19183134\n",
      "Iteration 5, loss = 0.15168950\n",
      "Iteration 6, loss = 0.11590822\n",
      "Iteration 7, loss = 0.08722532\n",
      "Iteration 8, loss = 0.06643215\n",
      "Iteration 9, loss = 0.06010377\n",
      "Iteration 10, loss = 0.03870813\n",
      "Iteration 11, loss = 0.03734853\n",
      "Iteration 12, loss = 0.02838144\n",
      "Iteration 13, loss = 0.01939096\n",
      "Iteration 14, loss = 0.01387954\n",
      "Iteration 15, loss = 0.00931380\n",
      "Iteration 16, loss = 0.00763214\n",
      "Iteration 17, loss = 0.00585316\n",
      "Iteration 18, loss = 0.00491232\n",
      "Iteration 19, loss = 0.00425903\n",
      "Iteration 20, loss = 0.00368346\n",
      "Iteration 21, loss = 0.00319634\n",
      "Iteration 22, loss = 0.00284732\n",
      "Iteration 23, loss = 0.00264543\n",
      "Iteration 24, loss = 0.00229924\n",
      "Iteration 25, loss = 0.00207189\n",
      "Iteration 26, loss = 0.00190824\n",
      "Iteration 27, loss = 0.00177816\n",
      "Iteration 28, loss = 0.00165566\n",
      "Iteration 29, loss = 0.00152598\n",
      "Iteration 30, loss = 0.00143222\n",
      "Iteration 31, loss = 0.00134631\n",
      "Iteration 32, loss = 0.00128139\n",
      "Iteration 33, loss = 0.00120965\n",
      "Iteration 34, loss = 0.00115140\n",
      "Iteration 35, loss = 0.00109138\n",
      "Iteration 36, loss = 0.00104006\n",
      "Iteration 37, loss = 0.00099402\n",
      "Iteration 38, loss = 0.00094825\n",
      "Iteration 39, loss = 0.00091613\n",
      "Iteration 40, loss = 0.00088111\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
     ]
    },
    {
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       "MLPClassifier(hidden_layer_sizes=(512, 256, 128), verbose=True)"
      ]
     },
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "ExecuteTime": {
     "end_time": "2021-06-10T01:17:53.794677Z",
     "start_time": "2021-06-10T01:17:53.784681Z"
    }
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"number.model\", \"wb\") as f:\n",
    "    pickle.dump(model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "end_time": "2021-06-10T01:19:46.346718Z",
     "start_time": "2021-06-10T01:19:44.656860Z"
    }
   },
   "outputs": [],
   "source": [
    "img4 = cv2.imread('./images/four.png', 0)\n",
    "cv2.imshow(\"4\", img4)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-10T01:20:06.491856Z",
     "start_time": "2021-06-10T01:20:06.480860Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n",
      "[4]\n"
     ]
    }
   ],
   "source": [
    "number = img4.reshape(-1, 400)\n",
    "\n",
    "import pickle\n",
    "with open(\"number.model\", \"rb\") as f:\n",
    "    model = pickle.load(f)\n",
    "\n",
    "print(model.predict_proba(number))\n",
    "print(model.predict(number))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-10T02:49:08.624187Z",
     "start_time": "2021-06-10T02:49:02.209922Z"
    }
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "if cap.isOpened():\n",
    "    while True:\n",
    "        ret, img = cap.read()\n",
    "        if not ret: break \n",
    "\n",
    "        g_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "        _, bin_img = cv2.threshold(g_img, 110, 255, cv2.THRESH_BINARY_INV)\n",
    "        contours, hierarchy = cv2.findContours(bin_img, cv2.RETR_EXTERNAL, \n",
    "                                               cv2.CHAIN_APPROX_SIMPLE)\n",
    "        for contour in contours: \n",
    "            (x,y), radius = cv2.minEnclosingCircle(contour)\n",
    "            if radius < 5: continue # 5보다 작은 객체 Skip \n",
    "            xs, xe = int(x-radius), int(x+radius)\n",
    "            ys, ye = int(y-radius), int(y+radius)\n",
    "            cv2.rectangle(bin_img, (xs,ys), (xe,ye), (200,0,0), 1)\n",
    "            roi = bin_img[ys:ye, xs:xe]\n",
    "            if roi.shape[0]*roi.shape[1] == 0: continue \n",
    "            roi = cv2.resize(roi, dsize=(16, 16))\n",
    "            A = np.zeros((20,20))\n",
    "            A[2:-2,2:-2] = roi[:,:]\n",
    "            A = A.reshape(-1,400)\n",
    "            num = model.predict(A)\n",
    "            cv2.putText(bin_img, str(num), (xs, ys), cv2.FONT_HERSHEY_PLAIN, 2, (200,0,0))\n",
    "        cv2.imshow(\"Image\", bin_img)\n",
    "        if cv2.waitKey(1)&0xFF == 27: break # ESC            \n",
    "else :\n",
    "    print(\"Camera not opened\")\n",
    "    \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     "end_time": "2021-06-10T01:41:10.268134Z",
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      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RandomForestClassifier를 이용해서 모델을 만들 수 있습니다.\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-10T01:41:27.909345Z",
     "start_time": "2021-06-10T01:41:27.843367Z"
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     "execution_count": 12,
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   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}