3x2 recogonizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2,os\n",
    "data_path='Dataset step 2/Object'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories=os.listdir(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'diya': 0, 'kalash': 1, 'sankh': 2}\n",
      "['diya', 'kalash', 'sankh']\n",
      "[0, 1, 2]\n"
     ]
    }
   ],
   "source": [
    "labels=[i for i in range(len(categories))]\n",
    "\n",
    "label_dict=dict(zip(categories,labels)) \n",
    "\n",
    "print(label_dict)\n",
    "print(categories)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_size=100\n",
    "data=[]\n",
    "target=[]\n",
    "\n",
    "\n",
    "for category in categories:\n",
    "    folder_path=os.path.join(data_path,category)\n",
    "    img_names=os.listdir(folder_path)\n",
    "        \n",
    "    for img_name in img_names:\n",
    "        img_path=os.path.join(folder_path,img_name)\n",
    "        img=cv2.imread(img_path)\n",
    "\n",
    "        try:\n",
    "            gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)           \n",
    "           \n",
    "            resized=cv2.resize(gray,(img_size,img_size))\n",
    "          \n",
    "            data.append(resized)\n",
    "            target.append(label_dict[category])\n",
    "           \n",
    "        except Exception as e:\n",
    "            print('Exception:',e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data=np.array(data)/255.0\n",
    "data=np.reshape(data,(data.shape[0],img_size,img_size,1))\n",
    "target=np.array(target)\n",
    "\n",
    "from keras.utils import np_utils\n",
    "\n",
    "new_target=np_utils.to_categorical(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('data',data)\n",
    "np.save('target',new_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "data=np.load('data.npy')\n",
    "target=np.load('target.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1900, 3), (1900, 100, 100, 1))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape, data.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "X1=data.reshape(data.shape[0],img_size,img_size)\n",
    "plt.imshow(X1[100]) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation,Flatten,Dropout\n",
    "from keras.layers import Conv2D,MaxPooling2D\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "model=Sequential()\n",
    "\n",
    "model.add(Conv2D(200,(3,3),input_shape=data.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "\n",
    "model.add(Conv2D(100,(3,3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(50,activation='relu'))\n",
    "\n",
    "model.add(Dense(3,activation='softmax'))\n",
    "\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data,test_data,train_target,test_target=train_test_split(data,target,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\sneha\\anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 1368 samples, validate on 342 samples\n",
      "Epoch 1/12\n",
      "1368/1368 [==============================] - 32s 23ms/step - loss: 0.9104 - accuracy: 0.5826 - val_loss: 0.7089 - val_accuracy: 0.7339\n",
      "Epoch 2/12\n",
      "1368/1368 [==============================] - 34s 25ms/step - loss: 0.6173 - accuracy: 0.7639 - val_loss: 0.5754 - val_accuracy: 0.7865\n",
      "Epoch 3/12\n",
      "1368/1368 [==============================] - 36s 26ms/step - loss: 0.5364 - accuracy: 0.7961 - val_loss: 0.5469 - val_accuracy: 0.8012\n",
      "Epoch 4/12\n",
      "1368/1368 [==============================] - 39s 28ms/step - loss: 0.4543 - accuracy: 0.8121 - val_loss: 0.4440 - val_accuracy: 0.8158\n",
      "Epoch 5/12\n",
      "1368/1368 [==============================] - 39s 29ms/step - loss: 0.3618 - accuracy: 0.8450 - val_loss: 0.4028 - val_accuracy: 0.8158\n",
      "Epoch 6/12\n",
      "1368/1368 [==============================] - 37s 27ms/step - loss: 0.3038 - accuracy: 0.8794 - val_loss: 0.3882 - val_accuracy: 0.8275\n",
      "Epoch 7/12\n",
      "1368/1368 [==============================] - 36s 26ms/step - loss: 0.2570 - accuracy: 0.8925 - val_loss: 0.3968 - val_accuracy: 0.8421\n",
      "Epoch 8/12\n",
      "1368/1368 [==============================] - 37s 27ms/step - loss: 0.2067 - accuracy: 0.9174 - val_loss: 0.3943 - val_accuracy: 0.8509\n",
      "Epoch 9/12\n",
      "1368/1368 [==============================] - 35s 25ms/step - loss: 0.1451 - accuracy: 0.9539 - val_loss: 0.3122 - val_accuracy: 0.8713\n",
      "Epoch 10/12\n",
      "1368/1368 [==============================] - 34s 25ms/step - loss: 0.1104 - accuracy: 0.9656 - val_loss: 0.3634 - val_accuracy: 0.8772\n",
      "Epoch 11/12\n",
      "1368/1368 [==============================] - 36s 26ms/step - loss: 0.0880 - accuracy: 0.9678 - val_loss: 0.3484 - val_accuracy: 0.8684\n",
      "Epoch 12/12\n",
      "1368/1368 [==============================] - 36s 26ms/step - loss: 0.0658 - accuracy: 0.9795 - val_loss: 0.3817 - val_accuracy: 0.8772\n"
     ]
    }
   ],
   "source": [
    "checkpoint = ModelCheckpoint('model-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')\n",
    "history=model.fit(train_data,train_target,epochs=12,callbacks=[checkpoint],validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(history.history['loss'],'r',label='training loss')\n",
    "plt.plot(history.history['val_loss'],label='validation loss')\n",
    "plt.xlabel('# epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190/190 [==============================] - 1s 6ms/step\n",
      "[0.3904369774617647, 0.8684210777282715]\n"
     ]
    }
   ],
   "source": [
    "print(model.evaluate(test_data,test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results={\n",
    "   0:'Symbols',\n",
    "   1:'Devices',\n",
    " \n",
    " \n",
    "}\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "im=Image.open(\"test.jpg\")\n",
    "display(im)\n",
    "\n",
    "im=cv2.cvtColor(np.float32(im),cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "im=cv2.resize(im,(img_size,img_size))\n",
    "im=np.resize(im,(1,100,100,1))\n",
    "im=np.array(im)/255.0\n",
    "pred=model.predict_classes([im])[0]\n",
    "print(pred,results[pred])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}