Train_Model.ipynb

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     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bd38de1-04e0-4b3c-a9f8-c26a7c5f76b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "from skimage.io import imread\n",
    "from skimage.transform import resize\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33aed754-2175-442b-9b37-e568aaf59bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tranform_image(img):\n",
    "    feature = []\n",
    "    tranform_img = resize(img, (150, 150))\n",
    "    flatten_img = tranform_img.flatten()\n",
    "    feature.append(flatten_img)\n",
    "    return np.array(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04dc7f06-5218-4702-88ba-92c03b2a507a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Get_Model:\n",
    "    def __init__(self,dir_,file_name):\n",
    "        self.dir_=dir_\n",
    "        self.file_name=file_name\n",
    "        \n",
    "    def get_data(self):\n",
    "        X=[list(map(tranform_image,[imread(self.dir_+img) for img in os.listdir(self.dir_)]))]\n",
    "        X=np.squeeze(X[0])\n",
    "        y=[int(img.split('-')[0].replace('img','')) for img in os.listdir(self.dir_)]\n",
    "        return X,y\n",
    "\n",
    "    def build_model(self):\n",
    "        X,y=self.get_data()\n",
    "        model=LogisticRegression()\n",
    "        model.fit(X,y)\n",
    "        print(model.score(X,y))\n",
    "        pickle.dump(model,open('Models/'+self.file_name+'.pkl','wb'),fix_imports=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee60d076-595f-49c6-8c3c-f70e1b8af81b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "o = Get_Model(\"test_img_aa/\", \"KCRaa\")\n",
    "o.build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dac5f7cf-fb87-4218-8185-013408f4abc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "o = Get_Model(\"test_img_ka/\", \"KCRkaa\")\n",
    "o.build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "abdec2b6-79a9-4392-8d42-1384cd666b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "o = Get_Model(\"test_img_num/\", \"KCRnum\")\n",
    "o.build_model()"
   ]
  }
 ],
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